{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:C2ZJ67GG3KCSUSESUSCKI33ZHQ","short_pith_number":"pith:C2ZJ67GG","schema_version":"1.0","canonical_sha256":"16b29f7cc6da852a4892a484a46f793c2d1ae2011fe99ce1663ea21e03f54111","source":{"kind":"arxiv","id":"2210.00864","version":1},"attestation_state":"computed","paper":{"title":"quEEGNet: Quantum AI for Biosignal Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"quant-ph","authors_text":"Toshiaki Koike-Akino, Ye Wang","submitted_at":"2022-09-29T01:59:24Z","abstract_excerpt":"In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC."},"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":"2210.00864","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-09-29T01:59:24Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"f0ecd307e07db443b292dcd71f70db4023a6e5c0c0b3a79e229b444d49194a1c","abstract_canon_sha256":"adf834ac30fe7b784cc9f67c264ec3acdeca984bc24ff6e5a6e83e39d41185a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:48.721055Z","signature_b64":"hhdBrRD8pduJpSWjq6MkqBgXi0Bvo8oM64XnbbLpyBCwd0d42oFMxhM4dcJkK6wfyy3JkdNKn4F+np9ZKbJABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16b29f7cc6da852a4892a484a46f793c2d1ae2011fe99ce1663ea21e03f54111","last_reissued_at":"2026-07-05T05:02:48.720677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:48.720677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"quEEGNet: Quantum AI for Biosignal Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"quant-ph","authors_text":"Toshiaki Koike-Akino, Ye Wang","submitted_at":"2022-09-29T01:59:24Z","abstract_excerpt":"In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.00864","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/2210.00864/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":"2210.00864","created_at":"2026-07-05T05:02:48.720753+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.00864v1","created_at":"2026-07-05T05:02:48.720753+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.00864","created_at":"2026-07-05T05:02:48.720753+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2ZJ67GG3KCS","created_at":"2026-07-05T05:02:48.720753+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2ZJ67GG3KCSUSES","created_at":"2026-07-05T05:02:48.720753+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2ZJ67GG","created_at":"2026-07-05T05:02:48.720753+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/C2ZJ67GG3KCSUSESUSCKI33ZHQ","json":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ.json","graph_json":"https://pith.science/api/pith-number/C2ZJ67GG3KCSUSESUSCKI33ZHQ/graph.json","events_json":"https://pith.science/api/pith-number/C2ZJ67GG3KCSUSESUSCKI33ZHQ/events.json","paper":"https://pith.science/paper/C2ZJ67GG"},"agent_actions":{"view_html":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ","download_json":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ.json","view_paper":"https://pith.science/paper/C2ZJ67GG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.00864&json=true","fetch_graph":"https://pith.science/api/pith-number/C2ZJ67GG3KCSUSESUSCKI33ZHQ/graph.json","fetch_events":"https://pith.science/api/pith-number/C2ZJ67GG3KCSUSESUSCKI33ZHQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ/action/storage_attestation","attest_author":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ/action/author_attestation","sign_citation":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ/action/citation_signature","submit_replication":"https://pith.science/pith/C2ZJ67GG3KCSUSESUSCKI33ZHQ/action/replication_record"}},"created_at":"2026-07-05T05:02:48.720753+00:00","updated_at":"2026-07-05T05:02:48.720753+00:00"}