{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SFHEJ5BMIUJZUZPWEPWNCXLJIL","short_pith_number":"pith:SFHEJ5BM","schema_version":"1.0","canonical_sha256":"914e44f42c45139a65f623ecd15d6942ee0fa5b1ed27473c4f855da46eb755df","source":{"kind":"arxiv","id":"2202.11727","version":1},"attestation_state":"computed","paper":{"title":"Completely Quantum Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","hep-ph","hep-th"],"primary_cat":"quant-ph","authors_text":"Juan C. Criado, Michael Spannowsky, Steve Abel","submitted_at":"2022-02-23T19:00:03Z","abstract_excerpt":"Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum anneale"},"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":"2202.11727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-02-23T19:00:03Z","cross_cats_sorted":["cs.LG","hep-ph","hep-th"],"title_canon_sha256":"bb896829b5a780f9e654fc6b4f69ec775ca1abee448bb5e03f7faaf89b438bd1","abstract_canon_sha256":"e914701af1b8d845f00b5f8307f48cf7d7f2c62ca5e2eb223dd50fe3f207ba60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:48:43.026890Z","signature_b64":"KSXGdxWw9CZQYx8rP4VP+VyhYGdCwlSk+Hv1S/O9s1bC7hIQCwvCcTN2Iz/rzsm0FayWOKeCHWPs72peduF2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"914e44f42c45139a65f623ecd15d6942ee0fa5b1ed27473c4f855da46eb755df","last_reissued_at":"2026-07-05T04:48:43.026457Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:48:43.026457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Completely Quantum Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","hep-ph","hep-th"],"primary_cat":"quant-ph","authors_text":"Juan C. Criado, Michael Spannowsky, Steve Abel","submitted_at":"2022-02-23T19:00:03Z","abstract_excerpt":"Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum anneale"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11727","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/2202.11727/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":"2202.11727","created_at":"2026-07-05T04:48:43.026524+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.11727v1","created_at":"2026-07-05T04:48:43.026524+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11727","created_at":"2026-07-05T04:48:43.026524+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFHEJ5BMIUJZ","created_at":"2026-07-05T04:48:43.026524+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFHEJ5BMIUJZUZPW","created_at":"2026-07-05T04:48:43.026524+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFHEJ5BM","created_at":"2026-07-05T04:48:43.026524+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.22801","citing_title":"Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL","json":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL.json","graph_json":"https://pith.science/api/pith-number/SFHEJ5BMIUJZUZPWEPWNCXLJIL/graph.json","events_json":"https://pith.science/api/pith-number/SFHEJ5BMIUJZUZPWEPWNCXLJIL/events.json","paper":"https://pith.science/paper/SFHEJ5BM"},"agent_actions":{"view_html":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL","download_json":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL.json","view_paper":"https://pith.science/paper/SFHEJ5BM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.11727&json=true","fetch_graph":"https://pith.science/api/pith-number/SFHEJ5BMIUJZUZPWEPWNCXLJIL/graph.json","fetch_events":"https://pith.science/api/pith-number/SFHEJ5BMIUJZUZPWEPWNCXLJIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL/action/storage_attestation","attest_author":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL/action/author_attestation","sign_citation":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL/action/citation_signature","submit_replication":"https://pith.science/pith/SFHEJ5BMIUJZUZPWEPWNCXLJIL/action/replication_record"}},"created_at":"2026-07-05T04:48:43.026524+00:00","updated_at":"2026-07-05T04:48:43.026524+00:00"}