{"as_of":"2026-08-04T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d3540fb2f3ee7a210210fb136d9960eb8f8aa1f0dbe1c4c1cf9473aaa800820e","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T07:06:14.748337Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2605.29877/citation-record","integrity":"/paper/2605.29877/integrity","json":"/paper/2605.29877/citation-record.json","paper":"/paper/2605.29877"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T07:06:14.748337Z","title":"Quantum machine learning.Nature, 549(7671):195–202, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:119815866b59bd7eb7341e476e94a39c1bd393a77f715e26c365c29d1de468d4","observation_id":"107da2e7-3dec-47ed-afff-2f3009013cb3","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Chal- lenges and opportunities in quantum machine learning.Nature computational science, 2(9):567– 576, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:90730d5585f3a918250c3c04fc8224954ecc00d713d82e8fe12127b22ba14ec3","observation_id":"9a0d29e8-af4d-4b02-aa22-76b841d64be7","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Córcoles, Kristan Temme, Aram W","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:92f291eb9c6740e442a9e96fcac3ac3aa6987316ecd47e3927f9ba3da5c597e7","observation_id":"f5de2abf-b252-4464-808f-9fa876857661","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"McMahon, Colin Scarato,FrancoisSwiadek,ChristianKraglundAndersen,ChristophHellings,SebastianKrinner, Nathan Lacroix, Stefania Lazar, Michael Kerschbaum, Dante Colao Zanuz, Graham J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:597d10db998c744a690c64e7381887b5f048fd46e720d6b0f53c7cd3da4e43c2","observation_id":"cfa71f6a-881c-4a3d-9a9b-4f8044b61f60","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Munro, Yong Heng Huo, Chao Yang Lu, Cheng Zhi Peng, Xiaobo Zhu, and Jian Wei Pan","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:1beb6a200f92d89051eb3e507491c1f00b2afe35e8a5dbe1e23fd68bcc94cc4a","observation_id":"b1785933-cba5-4284-988b-ef4fc787f294","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Anartificialneuron implemented on an actual quantum processor.npj Quantum Information, 5(1):26, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:b852e9d59b766ef0b81346de2c8d75cfff25f13407b4d50a243992b24663275b","observation_id":"28939b50-ffe3-46e4-9508-a2a16c9fcc3b","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Experimental quantum generative adversarial networks for image generation.Physical Review Applied, 16(2):024051, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4312cb2eb1732796ec8e0958dbadafbdc0fdfa999687a2b233c864b4a8215a90","observation_id":"84d6c9ca-fdfb-4edf-8642-cb403664502f","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Quantum generative adversarial networks with multiple superconducting qubits.npj Quantum Information, 7(1):165, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:99797d70e20471b02646c9bd0b6f756b50f64d4aabd1183757fc648e7d061c6e","observation_id":"4cc52e2e-6879-4123-b11e-e6b034a64873","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Experimental demonstration of quantum continual learning with superconducting qubits.npj Quantum Information, 12(1):28, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:0f0fa99f18d1a5f72e27db5374af005e708cb9dc8493d6f4e224bb9a1ec1315d","observation_id":"fd89d0f3-c5a2-4bf7-9578-94dce5342e75","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Quantumensemblelearningwithaprogrammablesuperconducting processor.npj Quantum Information, 11(1):83, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:f62ba596d8575df11c9b7387edb0e7bb159079588d7b5143853161923045502f","observation_id":"40673b13-f638-4a0a-aafc-0fe8f0e53128","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.02989","last_updated":"2021-08-26T18:00:02Z","snapshot_observed_at":"2026-08-04T14:51:09.502022Z","submitted_at":"2020-03-06T01:31:43Z","title":"TensorFlow Quantum: A Software Framework for Quantum Machine Learning","version":2},"cited_work":{"arxiv_id":"2003.02989","doi":"10.48550/arxiv.2003.02989","metadata_source":"arxiv_reference","pith_arxiv_id":"2003.02989","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Broughton, G","venue":null,"work_id":"821b7547-334f-417f-bd6a-2c4ce1910279","year":2003},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"cited_paper":"/paper/2003.02989","citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:b395f5758ada85c6327ac6a28b3ee04e85a1139a627a9de8707ba1dcf3d65317","observation_id":"c688a20b-b8c6-4340-b783-d17a7f7867b3","resolution":{"observed_at":"2026-06-29T07:13:16.782950Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T07:06:14.748337Z","title":"Quantumadversarialmachinelearning.Physical Review Research, 2(3):033212, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:dbf4e4b00c0e6f2762b719a67ae368a39e5b5c823584fb81df4e0de53ddd4b8f","observation_id":"b8fd1184-f063-4972-b2c4-f68f394681c1","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Vulnerability of quantum classification to adversarial perturbations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:686b6023e0df84dd405891b79126e4cae235e4b0f793a22c97d8077ed99437b3","observation_id":"2af27fcb-abd6-4dfb-b8d0-7402434c08ff","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Predominantaspectsonsecurityforquantummachinelearning:Lit- eraturereview","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:dc531f880ee394220849fa7083a4c0401352d436ff3076e02b217165c649a01b","observation_id":"22777a19-09fe-4141-860d-391b5f06d3c9","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Adversarialmachinelearning","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4253c3bda571f977567f784c5c660ef3cf6bc9adbc7f4b8ec44fdeaa59799838","observation_id":"d4fd260e-be03-4f3f-9715-9cbdc95d2dd3","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Explainingandharnessingadversarial examples","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:a9f1390f83647544e2a609dc63c3db245407e4d0bd5f323cd03b44ce62898b6a","observation_id":"ffe11ffe-1f2c-4d5f-9479-d8236a102f22","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Cam- bridge university press, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:712d04dee3717ace232b211d0b11c4c39f639ac554cc93507aafa91d333e7c09","observation_id":"5f6e6c3b-5781-4873-a3a1-be0baa95f374","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Robustness verification of quantum classifiers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:496e688d1a3754a924cc457e53e0ae50bfa102383e3defc66ead07bdc135d07a","observation_id":"c2ba4b11-f90a-4fc6-b462-ea39fe6c0d97","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"A robustness verification tool for quantum machine learning models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4f0c8711e8f761412ff2fd5b45d11d459998d75fdef9116f8fbeb15a43f4d402","observation_id":"f495d052-b6e4-4969-9354-95025da019a1","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Vasilakos, Yang Yang, Yu-Chun Wu, Ji Guan, Peng Duan, and Guo-Ping Guo","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:e6c0badaf3dccb77900e33a71c35ea2cf6dcb506770ac796c0fe51a865c466bd","observation_id":"e2b1be67-7dcf-4819-86cd-1e43ddd84108","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Reluplex: An efficient smt solver for verifying deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:2ea383e2c4268f4658f08409269337f0a53091409702080f60a451457600c6ac","observation_id":"04cab05b-df2a-48ac-a218-9c2686e6142d","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.07351","last_updated":"2017-06-22T14:59:49Z","snapshot_observed_at":"2026-07-06T05:48:00.958213Z","submitted_at":"2017-06-22T14:59:49Z","title":"An approach to reachability analysis for feed-forward ReLU neural networks","version":1},"cited_work":{"arxiv_id":"1706.07351","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.07351","snapshot_observed_at":"2026-07-03T00:27:29.911258Z","title":"An approach to reachability analysis for feed-forward ReLU neural networks","venue":"cs.AI","work_id":"1bcca84b-c816-459c-852a-5cbfac519f4b","year":2017},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"cited_paper":"/paper/1706.07351","citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:e5d1bd6e57f05b6829561e113a10103ccd98b2f52c00cc70d91402caab055fd8","observation_id":"ffae0769-2b3e-428c-865b-3eb7fe3efb30","resolution":{"observed_at":"2026-06-29T07:13:16.773082Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T07:06:14.748337Z","title":"Xiao, and Russ Tedrake","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:b01a828c1f851651fdae55277ae35c5947308e257a5f90c0a673772a4d78100c","observation_id":"8b44ff99-9644-4956-80f5-f5da5b4d34ab","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Measuring neural net robustness with constraints","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:ad2da524a9ec7cd73c13f2790a8c4308f3c1072f4d00dae8fa89bc9444a5d175","observation_id":"1e64bbff-e0f5-4cf7-95c9-55c4245517b2","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Verification of deep convolutional neural networks using imagestars","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:53b21f489fbda22b628fe6b442698e503c76897ca2c2c4d02e651d202388744c","observation_id":"24b224f3-051b-4e48-9368-5c375c20cc9b","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Adversarial attacks and defenses in deep learning.Engineering, 6(3):346–360, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4174b97c889c41656830fda98010a49b5672d7f248d4f1da145c75c8c3831141","observation_id":"7294ec52-dc30-4216-9a31-8dacc1350f24","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-07-06T04:04:16.777653Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":"1412.6572","doi":"10.48550/arxiv.1412.6572","metadata_source":"pith","pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-07-10T19:57:34.026407Z","title":"Explaining and Harnessing Adversarial Examples","venue":"stat.ML","work_id":"2cedf8f6-7539-4c49-8136-f42a20487146","year":2014},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4f47e71d959c1bd79f8c76eca4b371e56686c4caa09b1425121026fd226012a7","observation_id":"621017e4-2311-4b83-bf3d-7713f33ef28d","resolution":{"observed_at":"2026-06-29T07:13:16.787016Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T07:06:14.748337Z","title":"General parameter-shift rules for quantum gradients.Quantum, 6:677, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:2f546dd35439cdf80676ae74907c95e63dcec5886ef46c50e6295ad57af4bb94","observation_id":"668c1969-d7f9-4142-a6da-647b39b4029e","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Physical Review A, 98(3):032309, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:fcbc1127ae7c618b571a10b0d457a479f92ad48dbaced03eb8afc3c32ecd8806","observation_id":"73ec5592-0936-46b7-b5f2-a92dae6111da","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Evaluating analytic gradients on quantum hardware.Physical Review A, 99(3):032331, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:a9d6cbc279d41ad8986f866f334625d749630c35798e40dd293338ee9b31da50","observation_id":"92d1f3fc-e9c5-4fa2-bbd0-98a6cbbf3223","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"In2018 IEEE Conference on Decision and Control (CDC), pages 1624–1631","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:d8165c37bb81f09bcaf55d69596580837a8ee77ac40457139c3ec40ee6006df9","observation_id":"bae80276-8fae-43f8-9839-bdaf30cdac9f","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"An abstraction-based framework for neural network verification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:4f6b19accf356db126cfaad05dcf5d57fda19240dd39f7b45b0a0b1d9b19f13d","observation_id":"b2ef53f5-da41-4cda-be53-52b4611597c9","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Formal analysis and redesign of a neural network-based aircraft taxiing system with verifai","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:203ed443cee371dcf0ffb8502e183f558b2a125ff326d07e084ec7776dc24b51","observation_id":"d790527e-be5c-4e34-82ee-cb8e41c062b4","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Safetyverificationfordeepneuralnetworkswithprovableguarantees(in- vitedpaper)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:f7fbf96a100b2b035a3cdb9fdd86ec0e0527bdcd7b231103eba9780368cee688","observation_id":"88a07aec-8577-4da1-9fd7-34d2f5f8f4d2","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:88c57ec0236740da6694a73c348fd2e9da5ace8f8cf51803db5fc102a9531d27","observation_id":"4a3e334e-4d1d-4bd0-86a9-c46a360b8e51","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Prentice Hall Professional, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:dd1f13cd710eed8f34e34835e7fc40cca6ecc49e86b878f772ecb51cacbe7e52","observation_id":"2c2a3862-dd21-450a-a485-d13aac8886c3","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03429","last_updated":"2017-07-13T21:23:15Z","snapshot_observed_at":"2026-07-06T05:50:42.132417Z","submitted_at":"2017-07-11T18:49:44Z","title":"Open Quantum Assembly Language","version":2},"cited_work":{"arxiv_id":"1707.03429","doi":"10.48550/arxiv.1707.03429","metadata_source":"pith","pith_arxiv_id":"1707.03429","snapshot_observed_at":"2026-07-10T17:37:26.258884Z","title":"Open Quantum Assembly Language","venue":"quant-ph","work_id":"9b943b94-c4a0-4b7c-bfde-f18fdcc3660b","year":2017},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"cited_paper":"/paper/1707.03429","citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:5c97e70debbd3c50d0411a1d2b7eb8c7b79476cb79bbb445341486a2d0dea05f","observation_id":"4a84ccde-220d-42a4-bb43-3422041b5855","resolution":{"observed_at":"2026-06-29T07:13:16.777879Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T07:06:14.748337Z","title":"Quantumnoiseprotects quantum classifiers against adversaries.Physical Review Research, 3(2):023153, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:bce2d12c437d8c156d22350fb02fd58544890d69955759d8e2ae934fd4f14939","observation_id":"9a3f7b95-41e5-4a19-ae1c-557ee69b2441","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Verifying fairness in quantum machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:6e12149669b9247797d8f249d8e452d964ad976f219c03ab25d9d0dfbdbe1624","observation_id":"1560714a-8823-4643-ae0b-e2b156496708","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Certifiedrobustnessofquantumclassifiersagainstadversarialexamplesthrough quantum noise","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:e5dd7a5a285ce75192a66b54523aa216a29dba1dd05ee303fe950179739c702b","observation_id":"e697024c-27ba-4751-b331-87c35aa0cb97","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Emnist:Extendingmnist to handwritten letters","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:2cf7f18c1cb288e6975e4d7d1e24ad6c157741ca60f9f925fedb661568fcd50d","observation_id":"f528cf5e-74b7-46aa-9d30-aa1a99419a16","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Adversarial robustness of deep neural networks: A survey from a formal verification perspective.IEEE Transactions on Dependable and Secure Computing, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:402b5943e9131d3600d7d8efb35f1726c383f3db0fe87192356ca5fc67a984b4","observation_id":"9a907ce1-d318-4692-9fb2-439a2cc5c5d9","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Detecting violations of differen- tial privacy for quantum algorithms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:cb96900dc1c431c91bde86b274e3fbbfa74e3b9d0dd70ef67379938e38642d80","observation_id":"67dc36ec-25b0-4db2-862a-19afa049d934","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","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":"2026-06-29T07:06:14.748337Z","title":"Optimal mechanisms for quantum local differential privacy","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-29T07:06:14.748337Z"},"links":{"citing_paper":"/paper/2605.29877"},"observation_digest":"sha256:54f3c3db321eec60c90b5cb1ba5cdf798ec27aa8102d2533d39654e3c03c3259","observation_id":"9b870af6-303c-40b4-a80d-884b56b748f5","resolution":{"observed_at":"2026-06-29T07:06:14.748337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.29877","last_updated":"2026-05-28T13:00:48Z","latest_version":1,"primary_category":"quant-ph","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:00:48Z","title":"Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":40,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":44},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2605.29877."}