{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XP7JAFVNFKSKXVQZTM4F2KJ4CV","short_pith_number":"pith:XP7JAFVN","schema_version":"1.0","canonical_sha256":"bbfe9016ad2aa4abd6199b385d293c15665b8433cc4bc4baf511fadb01d4cc2d","source":{"kind":"arxiv","id":"2411.13378","version":2},"attestation_state":"computed","paper":{"title":"Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arabinda Kumar Choudhary, Hoang-Quan Nguyen, Hugh Churchill, Khoa Luu, Pawan Sinha, Samee U. Khan, Xuan-Bac Nguyen","submitted_at":"2024-11-20T14:59:47Z","abstract_excerpt":"Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usually introduced in a traditional learning paradigm missing the ability to learn the connectivities between brain regions. Meanwhile, the quantum computing theory offers a new paradigm for designing deep learning models. Motivated by the connectivities in the brain signals and the entanglement properties in quantum computing, we propose a novel Quantum-Brain approach, a quantum-inspired neural network, to tackle the visi"},"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":"2411.13378","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-20T14:59:47Z","cross_cats_sorted":[],"title_canon_sha256":"0108632d8d3c78bb073afa038d7d714fee8e249b6d39e104c4c58230b3cd1155","abstract_canon_sha256":"448a363c8daaec1600bfcfbca1324239971e0804b2fc90179d8c8950d5eaa000"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:42.676097Z","signature_b64":"FzaeuHiqP+hVesPuQlwuoBO1OGMwaau+mjMwWot6ke+83Akwaai0ZiaUyoKFx2t463od5januC2ReAyXqZ/qAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bbfe9016ad2aa4abd6199b385d293c15665b8433cc4bc4baf511fadb01d4cc2d","last_reissued_at":"2026-07-05T11:53:42.675565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:42.675565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arabinda Kumar Choudhary, Hoang-Quan Nguyen, Hugh Churchill, Khoa Luu, Pawan Sinha, Samee U. Khan, Xuan-Bac Nguyen","submitted_at":"2024-11-20T14:59:47Z","abstract_excerpt":"Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usually introduced in a traditional learning paradigm missing the ability to learn the connectivities between brain regions. Meanwhile, the quantum computing theory offers a new paradigm for designing deep learning models. Motivated by the connectivities in the brain signals and the entanglement properties in quantum computing, we propose a novel Quantum-Brain approach, a quantum-inspired neural network, to tackle the visi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13378","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/2411.13378/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":"2411.13378","created_at":"2026-07-05T11:53:42.675638+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.13378v2","created_at":"2026-07-05T11:53:42.675638+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13378","created_at":"2026-07-05T11:53:42.675638+00:00"},{"alias_kind":"pith_short_12","alias_value":"XP7JAFVNFKSK","created_at":"2026-07-05T11:53:42.675638+00:00"},{"alias_kind":"pith_short_16","alias_value":"XP7JAFVNFKSKXVQZ","created_at":"2026-07-05T11:53:42.675638+00:00"},{"alias_kind":"pith_short_8","alias_value":"XP7JAFVN","created_at":"2026-07-05T11:53:42.675638+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.18187","citing_title":"BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13833","citing_title":"QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV","json":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV.json","graph_json":"https://pith.science/api/pith-number/XP7JAFVNFKSKXVQZTM4F2KJ4CV/graph.json","events_json":"https://pith.science/api/pith-number/XP7JAFVNFKSKXVQZTM4F2KJ4CV/events.json","paper":"https://pith.science/paper/XP7JAFVN"},"agent_actions":{"view_html":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV","download_json":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV.json","view_paper":"https://pith.science/paper/XP7JAFVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.13378&json=true","fetch_graph":"https://pith.science/api/pith-number/XP7JAFVNFKSKXVQZTM4F2KJ4CV/graph.json","fetch_events":"https://pith.science/api/pith-number/XP7JAFVNFKSKXVQZTM4F2KJ4CV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV/action/storage_attestation","attest_author":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV/action/author_attestation","sign_citation":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV/action/citation_signature","submit_replication":"https://pith.science/pith/XP7JAFVNFKSKXVQZTM4F2KJ4CV/action/replication_record"}},"created_at":"2026-07-05T11:53:42.675638+00:00","updated_at":"2026-07-05T11:53:42.675638+00:00"}