{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AWMQT7W7S5WZ6WS2SXOGSY3X5X","short_pith_number":"pith:AWMQT7W7","schema_version":"1.0","canonical_sha256":"059909fedf976d9f5a5a95dc696377eddaec9af42bf02d87574529fba0e91b13","source":{"kind":"arxiv","id":"2504.00034","version":2},"attestation_state":"computed","paper":{"title":"Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Chi-Sheng Chen, Hsiang-Wei Hu, Wei An Hou, Zhen-Sheng Cai","submitted_at":"2025-03-30T06:36:22Z","abstract_excerpt":"Quantum generative models offer a promising new direction in machine learning by leveraging quantum circuits to enhance data generation capabilities. In this study, we propose a hybrid quantum-classical image generation framework that integrates variational quantum circuits into a diffusion-based model. To improve training dynamics and generation quality, we introduce two novel noise strategies: intrinsic quantum-generated noise and a tailored noise scheduling mechanism. Our method is built upon a lightweight U-Net architecture, with the quantum layer embedded in the bottleneck module to isola"},"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":"2504.00034","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-03-30T06:36:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c77e2b9d366af77820c864a6427c7f7221a3be59e782d9dd3cdd4626b54e169d","abstract_canon_sha256":"9e93c512cd53a549313e7ae6ce12c538a96fac8e65bdda02620cad8452e8a819"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:47.079926Z","signature_b64":"v3lXDyN6E67ztWDIzksZglZ5kAjy6PITRdQL1Bp2ZDcFdwRJGLKDableTGA2DcOz8mxVZOkICp+sbgmqbnNOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"059909fedf976d9f5a5a95dc696377eddaec9af42bf02d87574529fba0e91b13","last_reissued_at":"2026-07-05T10:43:47.079307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:47.079307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Chi-Sheng Chen, Hsiang-Wei Hu, Wei An Hou, Zhen-Sheng Cai","submitted_at":"2025-03-30T06:36:22Z","abstract_excerpt":"Quantum generative models offer a promising new direction in machine learning by leveraging quantum circuits to enhance data generation capabilities. In this study, we propose a hybrid quantum-classical image generation framework that integrates variational quantum circuits into a diffusion-based model. To improve training dynamics and generation quality, we introduce two novel noise strategies: intrinsic quantum-generated noise and a tailored noise scheduling mechanism. Our method is built upon a lightweight U-Net architecture, with the quantum layer embedded in the bottleneck module to isola"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.00034","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/2504.00034/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":"2504.00034","created_at":"2026-07-05T10:43:47.079414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.00034v2","created_at":"2026-07-05T10:43:47.079414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.00034","created_at":"2026-07-05T10:43:47.079414+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWMQT7W7S5WZ","created_at":"2026-07-05T10:43:47.079414+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWMQT7W7S5WZ6WS2","created_at":"2026-07-05T10:43:47.079414+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWMQT7W7","created_at":"2026-07-05T10:43:47.079414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06058","citing_title":"SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X","json":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X.json","graph_json":"https://pith.science/api/pith-number/AWMQT7W7S5WZ6WS2SXOGSY3X5X/graph.json","events_json":"https://pith.science/api/pith-number/AWMQT7W7S5WZ6WS2SXOGSY3X5X/events.json","paper":"https://pith.science/paper/AWMQT7W7"},"agent_actions":{"view_html":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X","download_json":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X.json","view_paper":"https://pith.science/paper/AWMQT7W7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.00034&json=true","fetch_graph":"https://pith.science/api/pith-number/AWMQT7W7S5WZ6WS2SXOGSY3X5X/graph.json","fetch_events":"https://pith.science/api/pith-number/AWMQT7W7S5WZ6WS2SXOGSY3X5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X/action/storage_attestation","attest_author":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X/action/author_attestation","sign_citation":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X/action/citation_signature","submit_replication":"https://pith.science/pith/AWMQT7W7S5WZ6WS2SXOGSY3X5X/action/replication_record"}},"created_at":"2026-07-05T10:43:47.079414+00:00","updated_at":"2026-07-05T10:43:47.079414+00:00"}