{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2E3XCMTPQMJIP4LT7EVA2DJ2VH","short_pith_number":"pith:2E3XCMTP","canonical_record":{"source":{"id":"2505.13205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-19T14:56:24Z","cross_cats_sorted":[],"title_canon_sha256":"9d55ee45d1e4cf411c6b6b97c763384526a13567ea42cea5f8a66f7521f4c7aa","abstract_canon_sha256":"046f7c52c58c995bfca63b0d4d4b87549aacfab44cad1cc8b4c215c495dea87f"},"schema_version":"1.0"},"canonical_sha256":"d13771326f831287f173f92a0d0d3aa9c4c126c762f21fe80176f47131aa72a3","source":{"kind":"arxiv","id":"2505.13205","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13205","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13205v2","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13205","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_12","alias_value":"2E3XCMTPQMJI","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_16","alias_value":"2E3XCMTPQMJIP4LT","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_8","alias_value":"2E3XCMTP","created_at":"2026-07-05T11:46:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2E3XCMTPQMJIP4LT7EVA2DJ2VH","target":"record","payload":{"canonical_record":{"source":{"id":"2505.13205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-19T14:56:24Z","cross_cats_sorted":[],"title_canon_sha256":"9d55ee45d1e4cf411c6b6b97c763384526a13567ea42cea5f8a66f7521f4c7aa","abstract_canon_sha256":"046f7c52c58c995bfca63b0d4d4b87549aacfab44cad1cc8b4c215c495dea87f"},"schema_version":"1.0"},"canonical_sha256":"d13771326f831287f173f92a0d0d3aa9c4c126c762f21fe80176f47131aa72a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:43.274766Z","signature_b64":"HLOxnxDGb3Xr1ucnWnjYiV1OAW7R6cpQ2UiQn2WcONxJuA1wnGiJihc7DmyKft7+99DxUkOWsX/cQMoP+9M2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d13771326f831287f173f92a0d0d3aa9c4c126c762f21fe80176f47131aa72a3","last_reissued_at":"2026-07-05T11:46:43.274112Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:43.274112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.13205","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:46:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ibBucXZN+ovgu9iq7V5FEcRppScVPJtnmFZVVyqhwCYxwOyAFNIZIpIV5imzJN0z51bsCH/rYYgvpoCiwKKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:50:29.300618Z"},"content_sha256":"7b2f030c73e8a6f7760879c6852267fd205dff97efcc5d6864e480394f6585c3","schema_version":"1.0","event_id":"sha256:7b2f030c73e8a6f7760879c6852267fd205dff97efcc5d6864e480394f6585c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2E3XCMTPQMJIP4LT7EVA2DJ2VH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantum Knowledge Distillation for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Fei Gao, Jiacheng Fan, Jing Li, Lingxiao Li, Qiaoyan Wen, Sujuan Qin, Yihao Wang","submitted_at":"2025-05-19T14:56:24Z","abstract_excerpt":"As foundational tools in natural language processing, Large Language Models (LLMs) have immense parameter scales, which makes deployment and inference increasingly prohibitive, especially in resource-constrained devices. Therefore, knowledge distillation for LLMs, i.e., compressing the LLM to a smaller model, is meaningful. With strong parameter representation capacity, quantum computing is regarded as a promising solution. Here, we propose a Quantum knowledge Distillation model for LLMs (QD-LLM) that leverages variational quantum circuits to learn from LLMs. In classical simulation, QD-LLM ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13205","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/2505.13205/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":"2026-07-05T11:46:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Si0xm8OtGjAhwy9pd9oZb/EEzVLIdokZaKEPiHnmByP3f7pjKOsmfianLF4cJ3CRs+IzPOrhsujTTRPTGnOEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:50:29.301291Z"},"content_sha256":"6ff558a3b577f2b1d7bc057763173b0f536af61f73363c0ed8cf4fc8e6e20d25","schema_version":"1.0","event_id":"sha256:6ff558a3b577f2b1d7bc057763173b0f536af61f73363c0ed8cf4fc8e6e20d25"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/bundle.json","state_url":"https://pith.science/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/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":"2026-08-07T15:50:29Z","links":{"resolver":"https://pith.science/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH","bundle":"https://pith.science/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/bundle.json","state":"https://pith.science/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2E3XCMTPQMJIP4LT7EVA2DJ2VH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2E3XCMTPQMJIP4LT7EVA2DJ2VH","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":"046f7c52c58c995bfca63b0d4d4b87549aacfab44cad1cc8b4c215c495dea87f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-19T14:56:24Z","title_canon_sha256":"9d55ee45d1e4cf411c6b6b97c763384526a13567ea42cea5f8a66f7521f4c7aa"},"schema_version":"1.0","source":{"id":"2505.13205","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13205","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13205v2","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13205","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_12","alias_value":"2E3XCMTPQMJI","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_16","alias_value":"2E3XCMTPQMJIP4LT","created_at":"2026-07-05T11:46:43Z"},{"alias_kind":"pith_short_8","alias_value":"2E3XCMTP","created_at":"2026-07-05T11:46:43Z"}],"graph_snapshots":[{"event_id":"sha256:6ff558a3b577f2b1d7bc057763173b0f536af61f73363c0ed8cf4fc8e6e20d25","target":"graph","created_at":"2026-07-05T11:46:43Z","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/2505.13205/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As foundational tools in natural language processing, Large Language Models (LLMs) have immense parameter scales, which makes deployment and inference increasingly prohibitive, especially in resource-constrained devices. Therefore, knowledge distillation for LLMs, i.e., compressing the LLM to a smaller model, is meaningful. With strong parameter representation capacity, quantum computing is regarded as a promising solution. Here, we propose a Quantum knowledge Distillation model for LLMs (QD-LLM) that leverages variational quantum circuits to learn from LLMs. In classical simulation, QD-LLM ou","authors_text":"Fei Gao, Jiacheng Fan, Jing Li, Lingxiao Li, Qiaoyan Wen, Sujuan Qin, Yihao Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-19T14:56:24Z","title":"Quantum Knowledge Distillation for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13205","kind":"arxiv","version":2},"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":"sha256:7b2f030c73e8a6f7760879c6852267fd205dff97efcc5d6864e480394f6585c3","target":"record","created_at":"2026-07-05T11:46:43Z","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":"046f7c52c58c995bfca63b0d4d4b87549aacfab44cad1cc8b4c215c495dea87f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-19T14:56:24Z","title_canon_sha256":"9d55ee45d1e4cf411c6b6b97c763384526a13567ea42cea5f8a66f7521f4c7aa"},"schema_version":"1.0","source":{"id":"2505.13205","kind":"arxiv","version":2}},"canonical_sha256":"d13771326f831287f173f92a0d0d3aa9c4c126c762f21fe80176f47131aa72a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d13771326f831287f173f92a0d0d3aa9c4c126c762f21fe80176f47131aa72a3","first_computed_at":"2026-07-05T11:46:43.274112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:46:43.274112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HLOxnxDGb3Xr1ucnWnjYiV1OAW7R6cpQ2UiQn2WcONxJuA1wnGiJihc7DmyKft7+99DxUkOWsX/cQMoP+9M2BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:46:43.274766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.13205","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b2f030c73e8a6f7760879c6852267fd205dff97efcc5d6864e480394f6585c3","sha256:6ff558a3b577f2b1d7bc057763173b0f536af61f73363c0ed8cf4fc8e6e20d25"],"state_sha256":"a0fe9563fd8d1aaf374087a851a7d1b470a8e387216a7d3d0de32e02efdd34da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jAZ8exmS9mqCOh4NIKsIhUK37IwCIafF99s8wIn7nCbLTSS/GpK4YmD2PuWsGJeD6+kvbRDb6eNdZsMhXRjkDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T15:50:29.305563Z","bundle_sha256":"204a47d7adaa0e2b7dbfa9cdcc4c59fdca3f9b2c42a9dd30629551ed8d13bd30"}}