{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MESPK74SLBN4YPKTLFJCTWW3OT","short_pith_number":"pith:MESPK74S","schema_version":"1.0","canonical_sha256":"6124f57f92585bcc3d53595229dadb74cef4a3352c8ed21036ff12a0bab066fd","source":{"kind":"arxiv","id":"2509.03233","version":2},"attestation_state":"computed","paper":{"title":"Scalable Entanglement Detection in Quantum Systems via Fisher Linear Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Mahmoud Mahdian, Zahra Mousavi","submitted_at":"2025-09-03T11:45:22Z","abstract_excerpt":"Quantum entanglement is the cornerstone of quantum technology and enables quantum devices to outperform classical systems in terms of performance. However, detecting entanglement in high-dimensional systems remains a significant challenge due to the exponential growth of the Hilbert space with the number of particles. In this work, we use machine learning to classify entangled states and separable states, focusing on the application of classical Fisher Linear Discriminant Analysis (FLDA). By adapting classical statistical learning techniques to quantum state discriminant analysis, we present t"},"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":"2509.03233","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-09-03T11:45:22Z","cross_cats_sorted":[],"title_canon_sha256":"e4fbb7247ab8d6694ea5aff495deb85af6f77aed239c42b8d66f894f2e7c47d2","abstract_canon_sha256":"f5d65cd5bf71e99ff567b374359cb295d9e4c0520973eb772775feeb451b4018"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:22.245384Z","signature_b64":"QzI5nqZOGAZv2bXrVTbdAESeSe3e3vIVPRran0m+AgFR8H6F3o9+nZkTDWg6QhiiLENjPqBWSS7BWgr78kUiDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6124f57f92585bcc3d53595229dadb74cef4a3352c8ed21036ff12a0bab066fd","last_reissued_at":"2026-07-05T12:05:22.244439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:22.244439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Entanglement Detection in Quantum Systems via Fisher Linear Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Mahmoud Mahdian, Zahra Mousavi","submitted_at":"2025-09-03T11:45:22Z","abstract_excerpt":"Quantum entanglement is the cornerstone of quantum technology and enables quantum devices to outperform classical systems in terms of performance. However, detecting entanglement in high-dimensional systems remains a significant challenge due to the exponential growth of the Hilbert space with the number of particles. In this work, we use machine learning to classify entangled states and separable states, focusing on the application of classical Fisher Linear Discriminant Analysis (FLDA). By adapting classical statistical learning techniques to quantum state discriminant analysis, we present t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.03233","kind":"arxiv","version":2},"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/2509.03233/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":"2509.03233","created_at":"2026-07-05T12:05:22.244500+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.03233v2","created_at":"2026-07-05T12:05:22.244500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.03233","created_at":"2026-07-05T12:05:22.244500+00:00"},{"alias_kind":"pith_short_12","alias_value":"MESPK74SLBN4","created_at":"2026-07-05T12:05:22.244500+00:00"},{"alias_kind":"pith_short_16","alias_value":"MESPK74SLBN4YPKT","created_at":"2026-07-05T12:05:22.244500+00:00"},{"alias_kind":"pith_short_8","alias_value":"MESPK74S","created_at":"2026-07-05T12:05:22.244500+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/MESPK74SLBN4YPKTLFJCTWW3OT","json":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT.json","graph_json":"https://pith.science/api/pith-number/MESPK74SLBN4YPKTLFJCTWW3OT/graph.json","events_json":"https://pith.science/api/pith-number/MESPK74SLBN4YPKTLFJCTWW3OT/events.json","paper":"https://pith.science/paper/MESPK74S"},"agent_actions":{"view_html":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT","download_json":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT.json","view_paper":"https://pith.science/paper/MESPK74S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.03233&json=true","fetch_graph":"https://pith.science/api/pith-number/MESPK74SLBN4YPKTLFJCTWW3OT/graph.json","fetch_events":"https://pith.science/api/pith-number/MESPK74SLBN4YPKTLFJCTWW3OT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT/action/storage_attestation","attest_author":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT/action/author_attestation","sign_citation":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT/action/citation_signature","submit_replication":"https://pith.science/pith/MESPK74SLBN4YPKTLFJCTWW3OT/action/replication_record"}},"created_at":"2026-07-05T12:05:22.244500+00:00","updated_at":"2026-07-05T12:05:22.244500+00:00"}