{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:T2QY3U5EF7PBSLS5ZAFUVYUWJ5","short_pith_number":"pith:T2QY3U5E","schema_version":"1.0","canonical_sha256":"9ea18dd3a42fde192e5dc80b4ae2964f4d7bbdd5cef0785507e896d026eacacc","source":{"kind":"arxiv","id":"2607.02767","version":1},"attestation_state":"computed","paper":{"title":"Self-Attention for Quantum Entanglement Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Anuj Gore, Dylan Lewis, Roopayan Ghosh, Sougato Bose","submitted_at":"2026-07-02T21:03:54Z","abstract_excerpt":"Quantum entanglement is a powerful resource for quantum-enhanced technologies. However, its reliable quantification remains challenging due to the exponential scaling of the Hilbert space with system size, which renders full state tomography infeasible. Moreover, experimentally estimating entanglement typically requires a large number of measurement samples leading to a significant overhead. In this work, we present two models, a feed-forward neural network and an attention-based model, to accurately predict the bipartite second Renyi from projective measurements of quantum states. We benchmar"},"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":"2607.02767","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-02T21:03:54Z","cross_cats_sorted":[],"title_canon_sha256":"65748aea90f2e10490b2ef597e967c7a5c82d01934f3a25e8c15e710d98a1bbf","abstract_canon_sha256":"460a1832ea097ada716a385ec9e9ac5b6469c4d882aee32c9835394e2e6f4406"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:16:12.861461Z","signature_b64":"uGXSjP4D0McOG9Um9I+9vQ3zhMyGCFH2uJQhT2FyXg2IcPqhK4U7Vsgz/7b5BJCprArUgHSPWC0ZoHAc+sMVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ea18dd3a42fde192e5dc80b4ae2964f4d7bbdd5cef0785507e896d026eacacc","last_reissued_at":"2026-07-07T00:16:12.860821Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:16:12.860821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Attention for Quantum Entanglement Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Anuj Gore, Dylan Lewis, Roopayan Ghosh, Sougato Bose","submitted_at":"2026-07-02T21:03:54Z","abstract_excerpt":"Quantum entanglement is a powerful resource for quantum-enhanced technologies. However, its reliable quantification remains challenging due to the exponential scaling of the Hilbert space with system size, which renders full state tomography infeasible. Moreover, experimentally estimating entanglement typically requires a large number of measurement samples leading to a significant overhead. In this work, we present two models, a feed-forward neural network and an attention-based model, to accurately predict the bipartite second Renyi from projective measurements of quantum states. We benchmar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02767","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/2607.02767/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":"2607.02767","created_at":"2026-07-07T00:16:12.860898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02767v1","created_at":"2026-07-07T00:16:12.860898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02767","created_at":"2026-07-07T00:16:12.860898+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2QY3U5EF7PB","created_at":"2026-07-07T00:16:12.860898+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2QY3U5EF7PBSLS5","created_at":"2026-07-07T00:16:12.860898+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2QY3U5E","created_at":"2026-07-07T00:16:12.860898+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/T2QY3U5EF7PBSLS5ZAFUVYUWJ5","json":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5.json","graph_json":"https://pith.science/api/pith-number/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/graph.json","events_json":"https://pith.science/api/pith-number/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/events.json","paper":"https://pith.science/paper/T2QY3U5E"},"agent_actions":{"view_html":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5","download_json":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5.json","view_paper":"https://pith.science/paper/T2QY3U5E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02767&json=true","fetch_graph":"https://pith.science/api/pith-number/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/action/storage_attestation","attest_author":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/action/author_attestation","sign_citation":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/action/citation_signature","submit_replication":"https://pith.science/pith/T2QY3U5EF7PBSLS5ZAFUVYUWJ5/action/replication_record"}},"created_at":"2026-07-07T00:16:12.860898+00:00","updated_at":"2026-07-07T00:16:12.860898+00:00"}