{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HCANONXAMOKWMTTSJER5YN7EVO","short_pith_number":"pith:HCANONXA","canonical_record":{"source":{"id":"2605.10258","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-11T09:24:12Z","cross_cats_sorted":[],"title_canon_sha256":"512a59d0df4de78a6ee38a7c034963d35ce4f1415bf016bdf21b9f04b3525680","abstract_canon_sha256":"f4c862c20a98f0e277b6bbf6c7bf66277c621034759ca339fbe8fdbdd398f26d"},"schema_version":"1.0"},"canonical_sha256":"3880d736e06395664e724923dc37e4abb02f89547bfc870274cfb8610da0711d","source":{"kind":"arxiv","id":"2605.10258","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.10258","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.10258v2","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.10258","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_12","alias_value":"HCANONXAMOKW","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_16","alias_value":"HCANONXAMOKWMTTS","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_8","alias_value":"HCANONXA","created_at":"2026-07-23T01:24:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HCANONXAMOKWMTTSJER5YN7EVO","target":"record","payload":{"canonical_record":{"source":{"id":"2605.10258","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-11T09:24:12Z","cross_cats_sorted":[],"title_canon_sha256":"512a59d0df4de78a6ee38a7c034963d35ce4f1415bf016bdf21b9f04b3525680","abstract_canon_sha256":"f4c862c20a98f0e277b6bbf6c7bf66277c621034759ca339fbe8fdbdd398f26d"},"schema_version":"1.0"},"canonical_sha256":"3880d736e06395664e724923dc37e4abb02f89547bfc870274cfb8610da0711d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T01:24:29.519184Z","signature_b64":"AcDAj3EqBEe712RxJaZT4Z8LTWDAqQtyWmuOh5TdskC37yjls8EPD/spI0/qpZpki2+p2v/VrrfRixXizWxUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3880d736e06395664e724923dc37e4abb02f89547bfc870274cfb8610da0711d","last_reissued_at":"2026-07-23T01:24:29.518294Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T01:24:29.518294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.10258","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-23T01:24:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9w6F6U/Y8Tm3GQSbcmv+Fc1AVD0sY7ZeJ0Y2hE5spG/y6V5TOs+qxT6hn71pauV747xG51Sh9bimUP3lkNjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:50:54.195340Z"},"content_sha256":"4c8e33ca7d8eedb5315103f56aab25265ab8a3347ddfb19ce72d768bea1edde1","schema_version":"1.0","event_id":"sha256:4c8e33ca7d8eedb5315103f56aab25265ab8a3347ddfb19ce72d768bea1edde1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HCANONXAMOKWMTTSJER5YN7EVO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parity Supervision as a Driver of Generalization in Quantum Generative Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments.","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Claudia Linnhoff-Popien, Daniel Hein, Jonas Stein, Markus Baumann, Steffen Udluft, Tobias Rohe","submitted_at":"2026-05-11T09:24:12Z","abstract_excerpt":"Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid but previously unseen states. In a controlled benchmark, we test whether parity-based training provides an inductive bias for this kind of generalization in instantaneous quantum polynomial-time (IQP) circuit Born machines. We compare the same IQP circuit trained with parity supervision and coordinate-wise mean-squared error (MSE), together with classical controls. Parity supervision improves exact distributional fi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Parity supervision improves exact forward Kullback-Leibler fit and unseen high-value-state recovery over IQP-MSE, while the maximum-entropy control does not reproduce the full effect. A parameter-free spectral reconstruction shows that parity moments already transfer evidence from observed samples to structurally compatible unseen states, which the IQP circuit further refines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The distribution to be learned, the parity objective, and the circuit architecture are structurally aligned; without this alignment the generalization benefit is not claimed to hold.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4ef8ce220358a3b9b51f130ebf84402d70bb5aab7bb53c111193abc7030165ba"},"source":{"id":"2605.10258","kind":"arxiv","version":2},"verdict":{"id":"a25621bb-599e-40b3-8bd2-8a1ac3ce71fe","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T05:22:12.005098Z","strongest_claim":"Parity supervision improves exact forward Kullback-Leibler fit and unseen high-value-state recovery over IQP-MSE, while the maximum-entropy control does not reproduce the full effect. A parameter-free spectral reconstruction shows that parity moments already transfer evidence from observed samples to structurally compatible unseen states, which the IQP circuit further refines.","one_line_summary":"Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The distribution to be learned, the parity objective, and the circuit architecture are structurally aligned; without this alignment the generalization benefit is not claimed to hold.","pith_extraction_headline":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.10258/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T06:22:00.872451Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T15:37:44.245858Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T11:31:19.573624Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T09:31:52.904798Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"7065718a7e207775a07481373e94caddbe6266c09757c39d82168036bdfac8f3"},"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":"a25621bb-599e-40b3-8bd2-8a1ac3ce71fe"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-23T01:24:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mtfdBhHKbY0/GWw3yLF40BUY7jpLo7OD7JG6PwfBLdUxH7jeT4C+mej8Vb7GUJNj3GJjyu86pxZ3aLN3YKAoAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:50:54.196162Z"},"content_sha256":"4525412748c3878a2bfe96cddf01668ad294851bd3190d648e20896f1533dbe4","schema_version":"1.0","event_id":"sha256:4525412748c3878a2bfe96cddf01668ad294851bd3190d648e20896f1533dbe4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HCANONXAMOKWMTTSJER5YN7EVO/bundle.json","state_url":"https://pith.science/pith/HCANONXAMOKWMTTSJER5YN7EVO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HCANONXAMOKWMTTSJER5YN7EVO/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":"2026-08-10T07:50:54Z","links":{"resolver":"https://pith.science/pith/HCANONXAMOKWMTTSJER5YN7EVO","bundle":"https://pith.science/pith/HCANONXAMOKWMTTSJER5YN7EVO/bundle.json","state":"https://pith.science/pith/HCANONXAMOKWMTTSJER5YN7EVO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HCANONXAMOKWMTTSJER5YN7EVO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HCANONXAMOKWMTTSJER5YN7EVO","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":"f4c862c20a98f0e277b6bbf6c7bf66277c621034759ca339fbe8fdbdd398f26d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-11T09:24:12Z","title_canon_sha256":"512a59d0df4de78a6ee38a7c034963d35ce4f1415bf016bdf21b9f04b3525680"},"schema_version":"1.0","source":{"id":"2605.10258","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.10258","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.10258v2","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.10258","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_12","alias_value":"HCANONXAMOKW","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_16","alias_value":"HCANONXAMOKWMTTS","created_at":"2026-07-23T01:24:29Z"},{"alias_kind":"pith_short_8","alias_value":"HCANONXA","created_at":"2026-07-23T01:24:29Z"}],"graph_snapshots":[{"event_id":"sha256:4525412748c3878a2bfe96cddf01668ad294851bd3190d648e20896f1533dbe4","target":"graph","created_at":"2026-07-23T01:24:29Z","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":"Parity supervision improves exact forward Kullback-Leibler fit and unseen high-value-state recovery over IQP-MSE, while the maximum-entropy control does not reproduce the full effect. A parameter-free spectral reconstruction shows that parity moments already transfer evidence from observed samples to structurally compatible unseen states, which the IQP circuit further refines."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The distribution to be learned, the parity objective, and the circuit architecture are structurally aligned; without this alignment the generalization benefit is not claimed to hold."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments."}],"snapshot_sha256":"4ef8ce220358a3b9b51f130ebf84402d70bb5aab7bb53c111193abc7030165ba"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-20T06:22:00.872451Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T15:37:44.245858Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T11:31:19.573624Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T09:31:52.904798Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.10258/integrity.json","findings":[],"snapshot_sha256":"7065718a7e207775a07481373e94caddbe6266c09757c39d82168036bdfac8f3","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid but previously unseen states. In a controlled benchmark, we test whether parity-based training provides an inductive bias for this kind of generalization in instantaneous quantum polynomial-time (IQP) circuit Born machines. We compare the same IQP circuit trained with parity supervision and coordinate-wise mean-squared error (MSE), together with classical controls. Parity supervision improves exact distributional fi","authors_text":"Claudia Linnhoff-Popien, Daniel Hein, Jonas Stein, Markus Baumann, Steffen Udluft, Tobias Rohe","cross_cats":[],"headline":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-11T09:24:12Z","title":"Parity Supervision as a Driver of Generalization in Quantum Generative Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.10258","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-12T05:22:12.005098Z","id":"a25621bb-599e-40b3-8bd2-8a1ac3ce71fe","model_set":{"reader":"grok-4.3"},"one_line_summary":"Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Parity supervision enables quantum Born machines to generalize from finite samples to unseen states by transferring evidence through parity moments.","strongest_claim":"Parity supervision improves exact forward Kullback-Leibler fit and unseen high-value-state recovery over IQP-MSE, while the maximum-entropy control does not reproduce the full effect. A parameter-free spectral reconstruction shows that parity moments already transfer evidence from observed samples to structurally compatible unseen states, which the IQP circuit further refines.","weakest_assumption":"The distribution to be learned, the parity objective, and the circuit architecture are structurally aligned; without this alignment the generalization benefit is not claimed to hold."}},"verdict_id":"a25621bb-599e-40b3-8bd2-8a1ac3ce71fe"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4c8e33ca7d8eedb5315103f56aab25265ab8a3347ddfb19ce72d768bea1edde1","target":"record","created_at":"2026-07-23T01:24:29Z","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":"f4c862c20a98f0e277b6bbf6c7bf66277c621034759ca339fbe8fdbdd398f26d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-11T09:24:12Z","title_canon_sha256":"512a59d0df4de78a6ee38a7c034963d35ce4f1415bf016bdf21b9f04b3525680"},"schema_version":"1.0","source":{"id":"2605.10258","kind":"arxiv","version":2}},"canonical_sha256":"3880d736e06395664e724923dc37e4abb02f89547bfc870274cfb8610da0711d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3880d736e06395664e724923dc37e4abb02f89547bfc870274cfb8610da0711d","first_computed_at":"2026-07-23T01:24:29.518294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-23T01:24:29.518294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AcDAj3EqBEe712RxJaZT4Z8LTWDAqQtyWmuOh5TdskC37yjls8EPD/spI0/qpZpki2+p2v/VrrfRixXizWxUCQ==","signature_status":"signed_v1","signed_at":"2026-07-23T01:24:29.519184Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.10258","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c8e33ca7d8eedb5315103f56aab25265ab8a3347ddfb19ce72d768bea1edde1","sha256:4525412748c3878a2bfe96cddf01668ad294851bd3190d648e20896f1533dbe4"],"state_sha256":"a423f3ed714a267afcf0ebdbcd7e1d6cbdef04071bceec65b707b3d4b6af460d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+jjzJjlrb9RdNSKPb7EzARZB4wN7DhqBGiXSTud/2Annca2f6aHZUg3gRUe6apTy388bW/JPxY3eQp2Z1ux8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T07:50:54.201671Z","bundle_sha256":"3206102c4c9fd6a9d30627f6d65d13bd256b3576c003d6890e846a702243ffe6"}}