{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3SBCFID24V22US2LLIJHH3UNC4","short_pith_number":"pith:3SBCFID2","schema_version":"1.0","canonical_sha256":"dc8222a07ae575aa4b4b5a1273ee8d173c4447299a79aad32ef5201720d85367","source":{"kind":"arxiv","id":"2509.14026","version":2},"attestation_state":"computed","paper":{"title":"Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Hsi-Sheng Goan, Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen","submitted_at":"2025-09-17T14:28:42Z","abstract_excerpt":"Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing the quantum variational activation function (QVAF), a general framework in which parameterized quantum circuits serve as learnable activation functions; in this work we study an efficient single-qubit instantiation called DatA Re-Uploading ActivatioN (DARUAN). We show that DARUAN with trainable data-preprocessing weights can realize an exponentially growing accessi"},"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":"2509.14026","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-09-17T14:28:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dca55cee1b3a44f7a14cb01604aef484f666197aa45cf287efd4ca5b70d1e388","abstract_canon_sha256":"26afcbe1510d6205e41ee7584c47894413fe742e88e16b19331eb3d8c11c10fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:19:40.631766Z","signature_b64":"//bWNfp+d7bn4OMuX/DpPD1cOqqjJN/+h3yq52P/WTk0CRmp5uZE3W8Li6jOGirv/HSnjm0gARm0KSw4npQvDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc8222a07ae575aa4b4b5a1273ee8d173c4447299a79aad32ef5201720d85367","last_reissued_at":"2026-07-07T02:19:40.630655Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:19:40.630655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Hsi-Sheng Goan, Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen","submitted_at":"2025-09-17T14:28:42Z","abstract_excerpt":"Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing the quantum variational activation function (QVAF), a general framework in which parameterized quantum circuits serve as learnable activation functions; in this work we study an efficient single-qubit instantiation called DatA Re-Uploading ActivatioN (DARUAN). We show that DARUAN with trainable data-preprocessing weights can realize an exponentially growing accessi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.14026","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/2509.14026/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":"2509.14026","created_at":"2026-07-07T02:19:40.630827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.14026v2","created_at":"2026-07-07T02:19:40.630827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.14026","created_at":"2026-07-07T02:19:40.630827+00:00"},{"alias_kind":"pith_short_12","alias_value":"3SBCFID24V22","created_at":"2026-07-07T02:19:40.630827+00:00"},{"alias_kind":"pith_short_16","alias_value":"3SBCFID24V22US2L","created_at":"2026-07-07T02:19:40.630827+00:00"},{"alias_kind":"pith_short_8","alias_value":"3SBCFID2","created_at":"2026-07-07T02:19:40.630827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2606.24933","citing_title":"Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning","ref_index":64,"is_internal_anchor":true},{"citing_arxiv_id":"2606.24932","citing_title":"Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2607.02363","citing_title":"Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2606.27821","citing_title":"Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2510.25781","citing_title":"A Practitioner's Guide to Kolmogorov-Arnold Networks","ref_index":54,"is_internal_anchor":true},{"citing_arxiv_id":"2605.06734","citing_title":"Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning","ref_index":50,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04604","citing_title":"Generative Quantum-inspired Kolmogorov-Arnold Eigensolver","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4","json":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4.json","graph_json":"https://pith.science/api/pith-number/3SBCFID24V22US2LLIJHH3UNC4/graph.json","events_json":"https://pith.science/api/pith-number/3SBCFID24V22US2LLIJHH3UNC4/events.json","paper":"https://pith.science/paper/3SBCFID2"},"agent_actions":{"view_html":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4","download_json":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4.json","view_paper":"https://pith.science/paper/3SBCFID2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.14026&json=true","fetch_graph":"https://pith.science/api/pith-number/3SBCFID24V22US2LLIJHH3UNC4/graph.json","fetch_events":"https://pith.science/api/pith-number/3SBCFID24V22US2LLIJHH3UNC4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4/action/storage_attestation","attest_author":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4/action/author_attestation","sign_citation":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4/action/citation_signature","submit_replication":"https://pith.science/pith/3SBCFID24V22US2LLIJHH3UNC4/action/replication_record"}},"created_at":"2026-07-07T02:19:40.630827+00:00","updated_at":"2026-07-07T02:19:40.630827+00:00"}