{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TYOAVPNE6M5AVWNNQOI7OWU5HR","short_pith_number":"pith:TYOAVPNE","schema_version":"1.0","canonical_sha256":"9e1c0abda4f33a0ad9ad8391f75a9d3c468381cf7d0499bbaa614c015aeb8129","source":{"kind":"arxiv","id":"2606.22713","version":1},"attestation_state":"computed","paper":{"title":"An interpretable closed form for entanglement entropy from bitstrings, guided by a graph neural network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Anas Saleh","submitted_at":"2026-06-21T23:19:53Z","abstract_excerpt":"The empirical bitstring distribution is the most accessible observable on Rydberg-atom arrays, but the bipartite von~Neumann entropy it constrains is far costlier to obtain. We present a six-term linear closed form for the entropy, built on bitstring-derivable physics scalars, and characterize its accuracy, portability, scaling behaviour, and calibration cost. The feature set is selected with guidance from a trained graph neural network: probing the network localizes its entropy prediction to the two-point correlators on the bipartition boundary, and an exhaustive ground-truth search restricte"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.22713","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-06-21T23:19:53Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"da92ab541d7ea8f7eecae912f86c5bed5179487a90f7b54180cf399de47159ba","abstract_canon_sha256":"a3a48536c774d4cf203223f6329b200b1e5f65a57a7e6010d5fcd1da000a0f68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:45.460658Z","signature_b64":"RycnUIS+0Yw4AV5Sh5oYQf74zHfX8MF6C2VQnA2+TPnBrwCABbok7j3+xW8mtgbasuBmZx0Ha8zU1DXH0A8IDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e1c0abda4f33a0ad9ad8391f75a9d3c468381cf7d0499bbaa614c015aeb8129","last_reissued_at":"2026-06-23T02:13:45.460235Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:45.460235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An interpretable closed form for entanglement entropy from bitstrings, guided by a graph neural network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Anas Saleh","submitted_at":"2026-06-21T23:19:53Z","abstract_excerpt":"The empirical bitstring distribution is the most accessible observable on Rydberg-atom arrays, but the bipartite von~Neumann entropy it constrains is far costlier to obtain. We present a six-term linear closed form for the entropy, built on bitstring-derivable physics scalars, and characterize its accuracy, portability, scaling behaviour, and calibration cost. The feature set is selected with guidance from a trained graph neural network: probing the network localizes its entropy prediction to the two-point correlators on the bipartition boundary, and an exhaustive ground-truth search restricte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22713","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/2606.22713/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.22713","created_at":"2026-06-23T02:13:45.460291+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22713v1","created_at":"2026-06-23T02:13:45.460291+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22713","created_at":"2026-06-23T02:13:45.460291+00:00"},{"alias_kind":"pith_short_12","alias_value":"TYOAVPNE6M5A","created_at":"2026-06-23T02:13:45.460291+00:00"},{"alias_kind":"pith_short_16","alias_value":"TYOAVPNE6M5AVWNN","created_at":"2026-06-23T02:13:45.460291+00:00"},{"alias_kind":"pith_short_8","alias_value":"TYOAVPNE","created_at":"2026-06-23T02:13:45.460291+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR","json":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR.json","graph_json":"https://pith.science/api/pith-number/TYOAVPNE6M5AVWNNQOI7OWU5HR/graph.json","events_json":"https://pith.science/api/pith-number/TYOAVPNE6M5AVWNNQOI7OWU5HR/events.json","paper":"https://pith.science/paper/TYOAVPNE"},"agent_actions":{"view_html":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR","download_json":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR.json","view_paper":"https://pith.science/paper/TYOAVPNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22713&json=true","fetch_graph":"https://pith.science/api/pith-number/TYOAVPNE6M5AVWNNQOI7OWU5HR/graph.json","fetch_events":"https://pith.science/api/pith-number/TYOAVPNE6M5AVWNNQOI7OWU5HR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR/action/storage_attestation","attest_author":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR/action/author_attestation","sign_citation":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR/action/citation_signature","submit_replication":"https://pith.science/pith/TYOAVPNE6M5AVWNNQOI7OWU5HR/action/replication_record"}},"created_at":"2026-06-23T02:13:45.460291+00:00","updated_at":"2026-06-23T02:13:45.460291+00:00"}