{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5F4B5ZXASWIFAZCHEVBDYKX6QP","short_pith_number":"pith:5F4B5ZXA","schema_version":"1.0","canonical_sha256":"e9781ee6e0959050644725423c2afe83cd7a9a008dd222b274c88fecd937b8fd","source":{"kind":"arxiv","id":"2503.22604","version":4},"attestation_state":"computed","paper":{"title":"Enhanced Variational Quantum Kolmogorov-Arnold Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Andriyan B. Suksmono, Hikaru Wakaura, Rahmat Mulyawan","submitted_at":"2025-03-28T16:47:42Z","abstract_excerpt":"The Kolmogorov-Arnold Network (KAN) places the trainable functions on the synapses rather than on the neurons. Existing quantum implementations either lack accuracy (Variational Quantum KAN, VQKAN) or rely on block encoding and Quantum Signal Processing, which demand many control gates and ancillae. We propose the Enhanced Variational Quantum Kolmogorov-Arnold Network (EVQKAN), a variational ansatz that emulates a $2^{N_q}$-dimensional KAN layer matrix by tiling controlled rotations through a sum-operator construction, using only $2^{N_q-1}$ trainable spline functions per layer. On the fitting"},"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":"2503.22604","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-03-28T16:47:42Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"925b68ef4817b9a6d7c5f5504828cc366f46bcfac834850f9e40c5daadfba367","abstract_canon_sha256":"4b8938dba9b704f5635c4fb7f4b51805ab6cb82c8754aaabc288ece5c00aec59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9781ee6e0959050644725423c2afe83cd7a9a008dd222b274c88fecd937b8fd","last_reissued_at":"2026-07-30T01:18:48.508730Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:18:48.508730Z"},"graph_snapshot":{"paper":{"title":"Enhanced Variational Quantum Kolmogorov-Arnold Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Andriyan B. Suksmono, Hikaru Wakaura, Rahmat Mulyawan","submitted_at":"2025-03-28T16:47:42Z","abstract_excerpt":"The Kolmogorov-Arnold Network (KAN) places the trainable functions on the synapses rather than on the neurons. Existing quantum implementations either lack accuracy (Variational Quantum KAN, VQKAN) or rely on block encoding and Quantum Signal Processing, which demand many control gates and ancillae. We propose the Enhanced Variational Quantum Kolmogorov-Arnold Network (EVQKAN), a variational ansatz that emulates a $2^{N_q}$-dimensional KAN layer matrix by tiling controlled rotations through a sum-operator construction, using only $2^{N_q-1}$ trainable spline functions per layer. On the fitting"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22604","kind":"arxiv","version":4},"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/2503.22604/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":"2503.22604","created_at":"2026-07-30T01:18:48.513937+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22604v4","created_at":"2026-07-30T01:18:48.513937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22604","created_at":"2026-07-30T01:18:48.513937+00:00"},{"alias_kind":"pith_short_12","alias_value":"5F4B5ZXASWIF","created_at":"2026-07-30T01:18:48.513937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5F4B5ZXASWIFAZCH","created_at":"2026-07-30T01:18:48.513937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5F4B5ZXA","created_at":"2026-07-30T01:18:48.513937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.09584","citing_title":"Algebraic Kolmogorov--Arnold representation theorem for quantum measurement","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP","json":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP.json","graph_json":"https://pith.science/api/pith-number/5F4B5ZXASWIFAZCHEVBDYKX6QP/graph.json","events_json":"https://pith.science/api/pith-number/5F4B5ZXASWIFAZCHEVBDYKX6QP/events.json","paper":"https://pith.science/paper/5F4B5ZXA"},"agent_actions":{"view_html":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP","download_json":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP.json","view_paper":"https://pith.science/paper/5F4B5ZXA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22604&json=true","fetch_graph":"https://pith.science/api/pith-number/5F4B5ZXASWIFAZCHEVBDYKX6QP/graph.json","fetch_events":"https://pith.science/api/pith-number/5F4B5ZXASWIFAZCHEVBDYKX6QP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP/action/storage_attestation","attest_author":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP/action/author_attestation","sign_citation":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP/action/citation_signature","submit_replication":"https://pith.science/pith/5F4B5ZXASWIFAZCHEVBDYKX6QP/action/replication_record"}},"created_at":"2026-07-30T01:18:48.513937+00:00","updated_at":"2026-07-30T01:18:48.513937+00:00"}