{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CY7DINUNWAZOPTGLB3TKWGFIUY","short_pith_number":"pith:CY7DINUN","schema_version":"1.0","canonical_sha256":"163e34368db032e7cccb0ee6ab18a8a6188d21f82c40ba88e5536b4278d5e66b","source":{"kind":"arxiv","id":"2506.11146","version":1},"attestation_state":"computed","paper":{"title":"HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"quant-ph","authors_text":"Jianhong Yao, Yangming Guo","submitted_at":"2025-06-11T09:12:20Z","abstract_excerpt":"Deep learning vision systems excel at pattern recognition yet falter when inputs are noisy or the model must explain its own confidence. Fuzzy inference, with its graded memberships and rule transparency, offers a remedy, while parameterized quantum circuits can embed features in richly entangled Hilbert spaces with striking parameter efficiency. Bridging these ideas, this study introduces a innovative Highly Quantized Fuzzy Neural Network (HQFNN) that realises the entire fuzzy pipeline inside a shallow quantum circuit and couples the resulting quantum signal to a lightweight CNN feature extra"},"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":"2506.11146","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-06-11T09:12:20Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"9c9aeaa3e8b99f907b3729e39cd5c7eb19f10d6cb9caffbea61c5248b4a9aa83","abstract_canon_sha256":"3fd1e0b8bb55d140621df270545e1fa251bf27b76c892e9d36babd0311e5ec9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:00.309900Z","signature_b64":"oED8kMEkxgSKifobjJF6OPB1tQzWiXNPbvTjxjpPK6yNwgBw4Wf2KeY+vLFVlMdo2pajoZxYQsw48SVHgD4vCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"163e34368db032e7cccb0ee6ab18a8a6188d21f82c40ba88e5536b4278d5e66b","last_reissued_at":"2026-07-05T11:21:00.309251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:00.309251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"quant-ph","authors_text":"Jianhong Yao, Yangming Guo","submitted_at":"2025-06-11T09:12:20Z","abstract_excerpt":"Deep learning vision systems excel at pattern recognition yet falter when inputs are noisy or the model must explain its own confidence. Fuzzy inference, with its graded memberships and rule transparency, offers a remedy, while parameterized quantum circuits can embed features in richly entangled Hilbert spaces with striking parameter efficiency. Bridging these ideas, this study introduces a innovative Highly Quantized Fuzzy Neural Network (HQFNN) that realises the entire fuzzy pipeline inside a shallow quantum circuit and couples the resulting quantum signal to a lightweight CNN feature extra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11146","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/2506.11146/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":"2506.11146","created_at":"2026-07-05T11:21:00.309308+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11146v1","created_at":"2026-07-05T11:21:00.309308+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11146","created_at":"2026-07-05T11:21:00.309308+00:00"},{"alias_kind":"pith_short_12","alias_value":"CY7DINUNWAZO","created_at":"2026-07-05T11:21:00.309308+00:00"},{"alias_kind":"pith_short_16","alias_value":"CY7DINUNWAZOPTGL","created_at":"2026-07-05T11:21:00.309308+00:00"},{"alias_kind":"pith_short_8","alias_value":"CY7DINUN","created_at":"2026-07-05T11:21:00.309308+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/CY7DINUNWAZOPTGLB3TKWGFIUY","json":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY.json","graph_json":"https://pith.science/api/pith-number/CY7DINUNWAZOPTGLB3TKWGFIUY/graph.json","events_json":"https://pith.science/api/pith-number/CY7DINUNWAZOPTGLB3TKWGFIUY/events.json","paper":"https://pith.science/paper/CY7DINUN"},"agent_actions":{"view_html":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY","download_json":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY.json","view_paper":"https://pith.science/paper/CY7DINUN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11146&json=true","fetch_graph":"https://pith.science/api/pith-number/CY7DINUNWAZOPTGLB3TKWGFIUY/graph.json","fetch_events":"https://pith.science/api/pith-number/CY7DINUNWAZOPTGLB3TKWGFIUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY/action/storage_attestation","attest_author":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY/action/author_attestation","sign_citation":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY/action/citation_signature","submit_replication":"https://pith.science/pith/CY7DINUNWAZOPTGLB3TKWGFIUY/action/replication_record"}},"created_at":"2026-07-05T11:21:00.309308+00:00","updated_at":"2026-07-05T11:21:00.309308+00:00"}