{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:EHMPTOGAP4TQVG7ZIYHLPFVCG7","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":"eff065cb10b962ed7f4a2482b3308b18edbb01d100587fbbdd1c15e3212234aa","cross_cats_sorted":["cs.NA","math.NA","q-fin.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-04-02T13:58:49Z","title_canon_sha256":"a079e4c8679e12b3f1653c3c094b4f69b1d2b7f6560f04ff19b2fca7780b3d3c"},"schema_version":"1.0","source":{"id":"2604.02064","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.02064","created_at":"2026-05-20T01:06:09Z"},{"alias_kind":"arxiv_version","alias_value":"2604.02064v3","created_at":"2026-05-20T01:06:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.02064","created_at":"2026-05-20T01:06:09Z"},{"alias_kind":"pith_short_12","alias_value":"EHMPTOGAP4TQ","created_at":"2026-05-20T01:06:09Z"},{"alias_kind":"pith_short_16","alias_value":"EHMPTOGAP4TQVG7Z","created_at":"2026-05-20T01:06:09Z"},{"alias_kind":"pith_short_8","alias_value":"EHMPTOGA","created_at":"2026-05-20T01:06:09Z"}],"graph_snapshots":[{"event_id":"sha256:c2148915cd5f7bdb0a90725343629c5ab00cab5b724f8aad1ed202b1c01a7755","target":"graph","created_at":"2026-05-20T01:06:09Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The noise model for the quantum neural networks and the representation of target functions as expectations allow the quantitative error bounds to be derived and hold."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A quantitative universal approximation theorem with error bounds is established for noisy quantum neural networks applied to expectation targets in finance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Noisy quantum neural networks can approximate continuous functions with explicit quantitative error bounds."}],"snapshot_sha256":"c0f82247a4b7da97495a34ed02360f1bf1dba93ef13a61f63c2343e89e51a4bd"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d334c3db8785f806c479f3a4600b860a199af49ba84557c4f70b0cd6beff8b88"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.02064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks. We focus on applications to Quantitative Finance, where target functions are often given as expectations. We further provide a detailed numerical analysis, testing our results on actual noisy quantum hardware.","authors_text":"Antoine Jacquier, Lukas Gonon, Marcel Mordarski","cross_cats":["cs.NA","math.NA","q-fin.PR"],"headline":"Noisy quantum neural networks can approximate continuous functions with explicit quantitative error bounds.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-04-02T13:58:49Z","title":"Quantitative Universal Approximation for Noisy Quantum Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.02064","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-13T21:27:45.149942Z","id":"602d6701-cd16-42a0-94ef-05d3015eeb4a","model_set":{"reader":"grok-4.3"},"one_line_summary":"A quantitative universal approximation theorem with error bounds is established for noisy quantum neural networks applied to expectation targets in finance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Noisy quantum neural networks can approximate continuous functions with explicit quantitative error bounds.","strongest_claim":"We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks.","weakest_assumption":"The noise model for the quantum neural networks and the representation of target functions as expectations allow the quantitative error bounds to be derived and hold."}},"verdict_id":"602d6701-cd16-42a0-94ef-05d3015eeb4a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c3e375a845a1e027870d0fb77b3dc71890446d58a15574009f8d5872864abced","target":"record","created_at":"2026-05-20T01:06:09Z","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":"eff065cb10b962ed7f4a2482b3308b18edbb01d100587fbbdd1c15e3212234aa","cross_cats_sorted":["cs.NA","math.NA","q-fin.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-04-02T13:58:49Z","title_canon_sha256":"a079e4c8679e12b3f1653c3c094b4f69b1d2b7f6560f04ff19b2fca7780b3d3c"},"schema_version":"1.0","source":{"id":"2604.02064","kind":"arxiv","version":3}},"canonical_sha256":"21d8f9b8c07f270a9bf9460eb796a237e00fb9335155e81322e221e12a5884e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21d8f9b8c07f270a9bf9460eb796a237e00fb9335155e81322e221e12a5884e0","first_computed_at":"2026-05-20T01:06:09.431165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:06:09.431165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jtOzR41WzQZDotZPOyDyGpfhCGI4LJNxKBd2QjXva/NVNMTOq68/aY0sx8io8QcS9d521iMRrSvLqpxUVb+NAA==","signature_status":"signed_v1","signed_at":"2026-05-20T01:06:09.431890Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.02064","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3e375a845a1e027870d0fb77b3dc71890446d58a15574009f8d5872864abced","sha256:c2148915cd5f7bdb0a90725343629c5ab00cab5b724f8aad1ed202b1c01a7755"],"state_sha256":"c1b851fa90dd64856de5d4c27706e0cfbd8a15fd8d7c46d0fef4dbcd97cd9538"}