{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KLPBKMMY5HRKRKPBZM4KTCSMZ5","short_pith_number":"pith:KLPBKMMY","schema_version":"1.0","canonical_sha256":"52de153198e9e2a8a9e1cb38a98a4ccf71e5f8c1df21d9f4feb4b6219e8cc041","source":{"kind":"arxiv","id":"2506.19270","version":1},"attestation_state":"computed","paper":{"title":"Continuous-variable Quantum Diffusion Model for State Generation and Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Chuangtao Chen, Haitao Huang, Qinglin Zhao","submitted_at":"2025-06-24T03:04:21Z","abstract_excerpt":"The generation and preservation of complex quantum states against environmental noise are paramount challenges in advancing continuous-variable (CV) quantum information processing. This paper introduces a novel framework based on continuous-variable quantum diffusion principles, synergizing them with CV quantum neural networks (CVQNNs) to address these dual challenges. For the task of state generation, our Continuous-Variable Quantum Diffusion Generative model (CVQD-G) employs a physically driven forward diffusion process using a thermal loss channel, which is then inverted by a learnable, par"},"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":"2506.19270","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-06-24T03:04:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"949e5e966000fbde32063153c44f7a2983010c01d9492183b0e6cfa4467a5f5e","abstract_canon_sha256":"0de85a5701a0ba757c983e50cbe2a1b968c98361fc5aad65a28af45e27e903fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:12.429074Z","signature_b64":"0tGDaFlLz2aa3+9FFuRprqPW5UN+Vrthh21HJ0ohrku4gcsXZYsLGFEQkNbZTQ1cvGWLeFGUybcjXBrvpTOUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52de153198e9e2a8a9e1cb38a98a4ccf71e5f8c1df21d9f4feb4b6219e8cc041","last_reissued_at":"2026-07-05T11:26:12.428609Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:12.428609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continuous-variable Quantum Diffusion Model for State Generation and Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Chuangtao Chen, Haitao Huang, Qinglin Zhao","submitted_at":"2025-06-24T03:04:21Z","abstract_excerpt":"The generation and preservation of complex quantum states against environmental noise are paramount challenges in advancing continuous-variable (CV) quantum information processing. This paper introduces a novel framework based on continuous-variable quantum diffusion principles, synergizing them with CV quantum neural networks (CVQNNs) to address these dual challenges. For the task of state generation, our Continuous-Variable Quantum Diffusion Generative model (CVQD-G) employs a physically driven forward diffusion process using a thermal loss channel, which is then inverted by a learnable, par"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.19270","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/2506.19270/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":"2506.19270","created_at":"2026-07-05T11:26:12.428672+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.19270v1","created_at":"2026-07-05T11:26:12.428672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.19270","created_at":"2026-07-05T11:26:12.428672+00:00"},{"alias_kind":"pith_short_12","alias_value":"KLPBKMMY5HRK","created_at":"2026-07-05T11:26:12.428672+00:00"},{"alias_kind":"pith_short_16","alias_value":"KLPBKMMY5HRKRKPB","created_at":"2026-07-05T11:26:12.428672+00:00"},{"alias_kind":"pith_short_8","alias_value":"KLPBKMMY","created_at":"2026-07-05T11:26:12.428672+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00365","citing_title":"When AI meets quantum information: A comprehensive review","ref_index":203,"is_internal_anchor":false},{"citing_arxiv_id":"2508.08799","citing_title":"Measurement-Based Quantum Diffusion Models","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5","json":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5.json","graph_json":"https://pith.science/api/pith-number/KLPBKMMY5HRKRKPBZM4KTCSMZ5/graph.json","events_json":"https://pith.science/api/pith-number/KLPBKMMY5HRKRKPBZM4KTCSMZ5/events.json","paper":"https://pith.science/paper/KLPBKMMY"},"agent_actions":{"view_html":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5","download_json":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5.json","view_paper":"https://pith.science/paper/KLPBKMMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.19270&json=true","fetch_graph":"https://pith.science/api/pith-number/KLPBKMMY5HRKRKPBZM4KTCSMZ5/graph.json","fetch_events":"https://pith.science/api/pith-number/KLPBKMMY5HRKRKPBZM4KTCSMZ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5/action/storage_attestation","attest_author":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5/action/author_attestation","sign_citation":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5/action/citation_signature","submit_replication":"https://pith.science/pith/KLPBKMMY5HRKRKPBZM4KTCSMZ5/action/replication_record"}},"created_at":"2026-07-05T11:26:12.428672+00:00","updated_at":"2026-07-05T11:26:12.428672+00:00"}