{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZFWN4TVZGAJZDZ2ONEUJGQ5BM7","short_pith_number":"pith:ZFWN4TVZ","canonical_record":{"source":{"id":"2506.20016","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T21:17:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"daab93134b65d976a5d4cf53ec3e37cdfb6d3c83b34f5fc0ac1e3121d9795bd9","abstract_canon_sha256":"097737730177ea3141116394ad484d04bf6604410a66a6df66166e86b9798f48"},"schema_version":"1.0"},"canonical_sha256":"c96cde4eb9301391e74e69289343a167db8d0d6326d3d690ebdda17fad7ed293","source":{"kind":"arxiv","id":"2506.20016","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.20016","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"arxiv_version","alias_value":"2506.20016v1","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20016","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_12","alias_value":"ZFWN4TVZGAJZ","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_16","alias_value":"ZFWN4TVZGAJZDZ2O","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_8","alias_value":"ZFWN4TVZ","created_at":"2026-07-05T11:26:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZFWN4TVZGAJZDZ2ONEUJGQ5BM7","target":"record","payload":{"canonical_record":{"source":{"id":"2506.20016","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T21:17:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"daab93134b65d976a5d4cf53ec3e37cdfb6d3c83b34f5fc0ac1e3121d9795bd9","abstract_canon_sha256":"097737730177ea3141116394ad484d04bf6604410a66a6df66166e86b9798f48"},"schema_version":"1.0"},"canonical_sha256":"c96cde4eb9301391e74e69289343a167db8d0d6326d3d690ebdda17fad7ed293","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:58.539385Z","signature_b64":"pc+CKSHMT1Ff1pS+JhfB476+UIJebuKgjV7jUfKDxQ3IoKBT0mfghyDQWzru0xjI8EpsLW/WvvCm1cWwbeiSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c96cde4eb9301391e74e69289343a167db8d0d6326d3d690ebdda17fad7ed293","last_reissued_at":"2026-07-05T11:26:58.538724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:58.538724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.20016","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:26:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3rlCx2T1w1XYiAKWa20QwqLmCTYc7qytIuFFTfd61+crGed9S9ED8vQpSucPfZyIijXMRvPrC+7PHUM29WPOCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:08:02.341458Z"},"content_sha256":"3af81fe0a691ef53d8dcda7f5e2e7e67aaeba3b727d54c1fbc1f1091a6e0553d","schema_version":"1.0","event_id":"sha256:3af81fe0a691ef53d8dcda7f5e2e7e67aaeba3b727d54c1fbc1f1091a6e0553d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZFWN4TVZGAJZDZ2ONEUJGQ5BM7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"New Insights on Unfolding and Fine-tuning Quantum Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel","submitted_at":"2025-06-24T21:17:48Z","abstract_excerpt":"Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep unfolding, which enables clients to autonomously optimize hyperparameters, such as learning rates and regularization factors, based on their specific training behavior. This dynamic adaptation mitigates overfitting and ensures robust optimization in highly heterogeneous environments where standard aggregation methods often fail. Our framework achieves approximately 90% accuracy, significantly outperforming traditional"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20016","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/2506.20016/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:26:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7/yCZEU2xH329iCl+Lv7qdIFyeM6PNpReqCxsyscAXKJRIXYniD8amd5frB4ovnwyCszMFbWSjSCJwAwAxDWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:08:02.342010Z"},"content_sha256":"0d5b06df28689ccca3b05202e67fc6483564386bb5a79c05f6d5cc6e17f71e8b","schema_version":"1.0","event_id":"sha256:0d5b06df28689ccca3b05202e67fc6483564386bb5a79c05f6d5cc6e17f71e8b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/bundle.json","state_url":"https://pith.science/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T09:08:02Z","links":{"resolver":"https://pith.science/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7","bundle":"https://pith.science/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/bundle.json","state":"https://pith.science/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZFWN4TVZGAJZDZ2ONEUJGQ5BM7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZFWN4TVZGAJZDZ2ONEUJGQ5BM7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"097737730177ea3141116394ad484d04bf6604410a66a6df66166e86b9798f48","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T21:17:48Z","title_canon_sha256":"daab93134b65d976a5d4cf53ec3e37cdfb6d3c83b34f5fc0ac1e3121d9795bd9"},"schema_version":"1.0","source":{"id":"2506.20016","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.20016","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"arxiv_version","alias_value":"2506.20016v1","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20016","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_12","alias_value":"ZFWN4TVZGAJZ","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_16","alias_value":"ZFWN4TVZGAJZDZ2O","created_at":"2026-07-05T11:26:58Z"},{"alias_kind":"pith_short_8","alias_value":"ZFWN4TVZ","created_at":"2026-07-05T11:26:58Z"}],"graph_snapshots":[{"event_id":"sha256:0d5b06df28689ccca3b05202e67fc6483564386bb5a79c05f6d5cc6e17f71e8b","target":"graph","created_at":"2026-07-05T11:26:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.20016/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep unfolding, which enables clients to autonomously optimize hyperparameters, such as learning rates and regularization factors, based on their specific training behavior. This dynamic adaptation mitigates overfitting and ensures robust optimization in highly heterogeneous environments where standard aggregation methods often fail. Our framework achieves approximately 90% accuracy, significantly outperforming traditional","authors_text":"Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T21:17:48Z","title":"New Insights on Unfolding and Fine-tuning Quantum Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20016","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3af81fe0a691ef53d8dcda7f5e2e7e67aaeba3b727d54c1fbc1f1091a6e0553d","target":"record","created_at":"2026-07-05T11:26:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"097737730177ea3141116394ad484d04bf6604410a66a6df66166e86b9798f48","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T21:17:48Z","title_canon_sha256":"daab93134b65d976a5d4cf53ec3e37cdfb6d3c83b34f5fc0ac1e3121d9795bd9"},"schema_version":"1.0","source":{"id":"2506.20016","kind":"arxiv","version":1}},"canonical_sha256":"c96cde4eb9301391e74e69289343a167db8d0d6326d3d690ebdda17fad7ed293","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c96cde4eb9301391e74e69289343a167db8d0d6326d3d690ebdda17fad7ed293","first_computed_at":"2026-07-05T11:26:58.538724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:26:58.538724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pc+CKSHMT1Ff1pS+JhfB476+UIJebuKgjV7jUfKDxQ3IoKBT0mfghyDQWzru0xjI8EpsLW/WvvCm1cWwbeiSDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:26:58.539385Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.20016","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3af81fe0a691ef53d8dcda7f5e2e7e67aaeba3b727d54c1fbc1f1091a6e0553d","sha256:0d5b06df28689ccca3b05202e67fc6483564386bb5a79c05f6d5cc6e17f71e8b"],"state_sha256":"7c014cf6e7f85e9bc0443b7fddca022672b9fa1cfbd1b626675e2faffccec4f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GSYOFZOi/w2aZ9sBJi/wNouCihIljLoikmEDjldlCWxR6OTiHVwLusaaA0LHSqwqd1kWs4ltEm+orTfCQZoNAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T09:08:02.346057Z","bundle_sha256":"ea7e449595bd319023d9e281851f60b6a3a2e58062585e7a7c2aa0fd5347ff6c"}}