{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R5TXYQCMHAFAHEAS4URIISMVOD","short_pith_number":"pith:R5TXYQCM","schema_version":"1.0","canonical_sha256":"8f677c404c380a039012e52284499570f95c9346618b8318a51461eb2685676f","source":{"kind":"arxiv","id":"2606.01291","version":1},"attestation_state":"computed","paper":{"title":"Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"quant-ph","authors_text":"Gregory T. Byrd, Syed Farhan Ahmad","submitted_at":"2026-05-31T15:23:10Z","abstract_excerpt":"Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\\mathcal{O}(1/2^n)$). This paper introduces and evaluates the Quantum Algorithm for Distributed Reduction of Entanglements (QADR), a hybrid quantum-classical machine learning framework that decomposes a global $n$-qubit VQC into localized sub-circuits operating approx"},"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":"2606.01291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-31T15:23:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6c2579ee767851499d94d77a5f38e891e5533d9409c0cb03bdd6c3080b2ecf93","abstract_canon_sha256":"ff8db0640b0ab81a1808d1d72cdfbcd24d5f4880a11a88165f35b654c72cafc0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:29.191441Z","signature_b64":"dMLkT9tSl5uNhkFDbyMnRDu6fx2ytppCRC3QYPsZ6M3XCIXuyE5UCWOcSTcKy/emZPPpTSVGj4NPkmVuFpawDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f677c404c380a039012e52284499570f95c9346618b8318a51461eb2685676f","last_reissued_at":"2026-06-02T02:04:29.191050Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:29.191050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"quant-ph","authors_text":"Gregory T. Byrd, Syed Farhan Ahmad","submitted_at":"2026-05-31T15:23:10Z","abstract_excerpt":"Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\\mathcal{O}(1/2^n)$). This paper introduces and evaluates the Quantum Algorithm for Distributed Reduction of Entanglements (QADR), a hybrid quantum-classical machine learning framework that decomposes a global $n$-qubit VQC into localized sub-circuits operating approx"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01291","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/2606.01291/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":"2606.01291","created_at":"2026-06-02T02:04:29.191105+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01291v1","created_at":"2026-06-02T02:04:29.191105+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01291","created_at":"2026-06-02T02:04:29.191105+00:00"},{"alias_kind":"pith_short_12","alias_value":"R5TXYQCMHAFA","created_at":"2026-06-02T02:04:29.191105+00:00"},{"alias_kind":"pith_short_16","alias_value":"R5TXYQCMHAFAHEAS","created_at":"2026-06-02T02:04:29.191105+00:00"},{"alias_kind":"pith_short_8","alias_value":"R5TXYQCM","created_at":"2026-06-02T02:04:29.191105+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/R5TXYQCMHAFAHEAS4URIISMVOD","json":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD.json","graph_json":"https://pith.science/api/pith-number/R5TXYQCMHAFAHEAS4URIISMVOD/graph.json","events_json":"https://pith.science/api/pith-number/R5TXYQCMHAFAHEAS4URIISMVOD/events.json","paper":"https://pith.science/paper/R5TXYQCM"},"agent_actions":{"view_html":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD","download_json":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD.json","view_paper":"https://pith.science/paper/R5TXYQCM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01291&json=true","fetch_graph":"https://pith.science/api/pith-number/R5TXYQCMHAFAHEAS4URIISMVOD/graph.json","fetch_events":"https://pith.science/api/pith-number/R5TXYQCMHAFAHEAS4URIISMVOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD/action/storage_attestation","attest_author":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD/action/author_attestation","sign_citation":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD/action/citation_signature","submit_replication":"https://pith.science/pith/R5TXYQCMHAFAHEAS4URIISMVOD/action/replication_record"}},"created_at":"2026-06-02T02:04:29.191105+00:00","updated_at":"2026-06-02T02:04:29.191105+00:00"}