{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RKUOSF5BTCQM2LINXVMCXDFN54","short_pith_number":"pith:RKUOSF5B","schema_version":"1.0","canonical_sha256":"8aa8e917a198a0cd2d0dbd582b8cadef139f45b1faa3fe0a141d933c8b076129","source":{"kind":"arxiv","id":"2505.18656","version":1},"attestation_state":"computed","paper":{"title":"LLM-QFL: Distilling Large Language Model for Quantum Federated Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dev Gurung, Shiva Raj Pokhrel","submitted_at":"2025-05-24T11:49:21Z","abstract_excerpt":"Inspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine-tuning method that distills an LLM within QFL, allowing each client to locally adapt the model to its own data while preserving privacy and reducing unnecessary global updates. The fine-tuned LLM also acts as a reinforcement agent, optimizing QFL by adjusting optimizer steps, cutting down communication rounds, and intelligently selecting clients. Experiments show significant efficiency gains. We pioneer a synergy be"},"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":"2505.18656","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T11:49:21Z","cross_cats_sorted":[],"title_canon_sha256":"29ab571c551deba142e54b20bd497aa77d459135c005b0fb29772e8e3c5d3be8","abstract_canon_sha256":"07deefae2ac398c805b594fa06efa9446591ef163faa59fd1ea88784db2c7bd4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:17.172439Z","signature_b64":"QE0+4uDU300cisPD/TMI1jXG+uQ8pS59SMRkTm6vHRdSGaWy8+DNry8DuAx2DY6CYuvmmxC8jfdlCGWFozl1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8aa8e917a198a0cd2d0dbd582b8cadef139f45b1faa3fe0a141d933c8b076129","last_reissued_at":"2026-07-05T11:09:17.171975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:17.171975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM-QFL: Distilling Large Language Model for Quantum Federated Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dev Gurung, Shiva Raj Pokhrel","submitted_at":"2025-05-24T11:49:21Z","abstract_excerpt":"Inspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine-tuning method that distills an LLM within QFL, allowing each client to locally adapt the model to its own data while preserving privacy and reducing unnecessary global updates. The fine-tuned LLM also acts as a reinforcement agent, optimizing QFL by adjusting optimizer steps, cutting down communication rounds, and intelligently selecting clients. Experiments show significant efficiency gains. We pioneer a synergy be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18656","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/2505.18656/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":"2505.18656","created_at":"2026-07-05T11:09:17.172024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.18656v1","created_at":"2026-07-05T11:09:17.172024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18656","created_at":"2026-07-05T11:09:17.172024+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKUOSF5BTCQM","created_at":"2026-07-05T11:09:17.172024+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKUOSF5BTCQM2LIN","created_at":"2026-07-05T11:09:17.172024+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKUOSF5B","created_at":"2026-07-05T11:09:17.172024+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/RKUOSF5BTCQM2LINXVMCXDFN54","json":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54.json","graph_json":"https://pith.science/api/pith-number/RKUOSF5BTCQM2LINXVMCXDFN54/graph.json","events_json":"https://pith.science/api/pith-number/RKUOSF5BTCQM2LINXVMCXDFN54/events.json","paper":"https://pith.science/paper/RKUOSF5B"},"agent_actions":{"view_html":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54","download_json":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54.json","view_paper":"https://pith.science/paper/RKUOSF5B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.18656&json=true","fetch_graph":"https://pith.science/api/pith-number/RKUOSF5BTCQM2LINXVMCXDFN54/graph.json","fetch_events":"https://pith.science/api/pith-number/RKUOSF5BTCQM2LINXVMCXDFN54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54/action/storage_attestation","attest_author":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54/action/author_attestation","sign_citation":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54/action/citation_signature","submit_replication":"https://pith.science/pith/RKUOSF5BTCQM2LINXVMCXDFN54/action/replication_record"}},"created_at":"2026-07-05T11:09:17.172024+00:00","updated_at":"2026-07-05T11:09:17.172024+00:00"}