{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VUDLAQYH6LI2KWJW7F7MBQFAU6","short_pith_number":"pith:VUDLAQYH","canonical_record":{"source":{"id":"2407.20753","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T11:55:52Z","cross_cats_sorted":["cs.AI","quant-ph"],"title_canon_sha256":"82cbb73bea3e1f7cdec3db54ed295b27c597560c681680b04e6e0b7b950ab8bc","abstract_canon_sha256":"0050913f524fc3b256e27ea29efb3b08ea7d781bd988db4f63c65ee3ea012496"},"schema_version":"1.0"},"canonical_sha256":"ad06b04307f2d1a55936f97ec0c0a0a78ef7f96ca77e0bed49230da20365a676","source":{"kind":"arxiv","id":"2407.20753","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.20753","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.20753v1","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.20753","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_12","alias_value":"VUDLAQYH6LI2","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_16","alias_value":"VUDLAQYH6LI2KWJW","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_8","alias_value":"VUDLAQYH","created_at":"2026-07-05T08:50:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VUDLAQYH6LI2KWJW7F7MBQFAU6","target":"record","payload":{"canonical_record":{"source":{"id":"2407.20753","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T11:55:52Z","cross_cats_sorted":["cs.AI","quant-ph"],"title_canon_sha256":"82cbb73bea3e1f7cdec3db54ed295b27c597560c681680b04e6e0b7b950ab8bc","abstract_canon_sha256":"0050913f524fc3b256e27ea29efb3b08ea7d781bd988db4f63c65ee3ea012496"},"schema_version":"1.0"},"canonical_sha256":"ad06b04307f2d1a55936f97ec0c0a0a78ef7f96ca77e0bed49230da20365a676","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:18.038365Z","signature_b64":"PHwOJd+ybDKIZjo2nMbRQB2qYbglyi2JNTRH+CMApFqpydjkZHtrsnkSpiOz57Yxto7l51tqcbL28XYmeFvMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad06b04307f2d1a55936f97ec0c0a0a78ef7f96ca77e0bed49230da20365a676","last_reissued_at":"2026-07-05T08:50:18.037796Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:18.037796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.20753","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:50:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WbVskIttqYXMN7/cvyrcQnpvEcD35tEZ/kAcfxm60VK/veIYbBz4Sc2BQudKI0XX57yW1WzqRWFrUFCiir7qBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:56:46.197874Z"},"content_sha256":"ef7b728456cd45d6a0868a714eae0e2581c1f0ee30bbcf6e4a22f51449a82871","schema_version":"1.0","event_id":"sha256:ef7b728456cd45d6a0868a714eae0e2581c1f0ee30bbcf6e4a22f51449a82871"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VUDLAQYH6LI2KWJW7F7MBQFAU6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","quant-ph"],"primary_cat":"cs.LG","authors_text":"Afrae Ahouzi, Claudia Linnhoff-Popien, Dani\\\"elle Schuman, Elif \\c{C}etiner, Michael K\\\"olle, Pascal Debus, Robert M\\\"uller","submitted_at":"2024-07-30T11:55:52Z","abstract_excerpt":"Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large datasets. In recent work, quantum randomized measurements kernels and variable subsampling were proposed, as two independent methods to address this problem. The former achieves higher average precision, but suffers from variance, while the latter achieves linear complexity to data size and has lower variance. The current work focuses instead on combining thes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.20753","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.20753/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:50:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p7MgwLWvKxAcIFOrd43gr/X+ieFgwXJt1zScc8tzODLoAuFxMEQHq7hcXoE6f3//d8r3P4uwvoAnf1pIHDqrAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:56:46.199521Z"},"content_sha256":"75dd3d95900ff55e8954324e9f3abfb5b6c2c1382122c2671cb70730c38316b3","schema_version":"1.0","event_id":"sha256:75dd3d95900ff55e8954324e9f3abfb5b6c2c1382122c2671cb70730c38316b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/bundle.json","state_url":"https://pith.science/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T15:56:46Z","links":{"resolver":"https://pith.science/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6","bundle":"https://pith.science/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/bundle.json","state":"https://pith.science/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VUDLAQYH6LI2KWJW7F7MBQFAU6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VUDLAQYH6LI2KWJW7F7MBQFAU6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0050913f524fc3b256e27ea29efb3b08ea7d781bd988db4f63c65ee3ea012496","cross_cats_sorted":["cs.AI","quant-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T11:55:52Z","title_canon_sha256":"82cbb73bea3e1f7cdec3db54ed295b27c597560c681680b04e6e0b7b950ab8bc"},"schema_version":"1.0","source":{"id":"2407.20753","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.20753","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.20753v1","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.20753","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_12","alias_value":"VUDLAQYH6LI2","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_16","alias_value":"VUDLAQYH6LI2KWJW","created_at":"2026-07-05T08:50:18Z"},{"alias_kind":"pith_short_8","alias_value":"VUDLAQYH","created_at":"2026-07-05T08:50:18Z"}],"graph_snapshots":[{"event_id":"sha256:75dd3d95900ff55e8954324e9f3abfb5b6c2c1382122c2671cb70730c38316b3","target":"graph","created_at":"2026-07-05T08:50:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.20753/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large datasets. In recent work, quantum randomized measurements kernels and variable subsampling were proposed, as two independent methods to address this problem. The former achieves higher average precision, but suffers from variance, while the latter achieves linear complexity to data size and has lower variance. The current work focuses instead on combining thes","authors_text":"Afrae Ahouzi, Claudia Linnhoff-Popien, Dani\\\"elle Schuman, Elif \\c{C}etiner, Michael K\\\"olle, Pascal Debus, Robert M\\\"uller","cross_cats":["cs.AI","quant-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T11:55:52Z","title":"Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.20753","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ef7b728456cd45d6a0868a714eae0e2581c1f0ee30bbcf6e4a22f51449a82871","target":"record","created_at":"2026-07-05T08:50:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0050913f524fc3b256e27ea29efb3b08ea7d781bd988db4f63c65ee3ea012496","cross_cats_sorted":["cs.AI","quant-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T11:55:52Z","title_canon_sha256":"82cbb73bea3e1f7cdec3db54ed295b27c597560c681680b04e6e0b7b950ab8bc"},"schema_version":"1.0","source":{"id":"2407.20753","kind":"arxiv","version":1}},"canonical_sha256":"ad06b04307f2d1a55936f97ec0c0a0a78ef7f96ca77e0bed49230da20365a676","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad06b04307f2d1a55936f97ec0c0a0a78ef7f96ca77e0bed49230da20365a676","first_computed_at":"2026-07-05T08:50:18.037796Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:50:18.037796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PHwOJd+ybDKIZjo2nMbRQB2qYbglyi2JNTRH+CMApFqpydjkZHtrsnkSpiOz57Yxto7l51tqcbL28XYmeFvMDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:50:18.038365Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.20753","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef7b728456cd45d6a0868a714eae0e2581c1f0ee30bbcf6e4a22f51449a82871","sha256:75dd3d95900ff55e8954324e9f3abfb5b6c2c1382122c2671cb70730c38316b3"],"state_sha256":"7829bfbdac286d4d4780c6bc32a51d8ba4e46e3e019ac551f237b9bca7c7a584"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kzaBNjyIkjOa5kQ0p8EQP/ik79/1nLq+o5tYRw6KJp0yAROKE1m5dwqfGXOAsYKfW/cl/RQ0NKBPVYPMRQNVBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T15:56:46.207157Z","bundle_sha256":"81a5b1ae8ad1d143a9e016b17ee9295d937153ca7fb8bdc562e3c6833942118a"}}