{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BSE674ZMNVNSLA4C6INHBYVMMX","short_pith_number":"pith:BSE674ZM","schema_version":"1.0","canonical_sha256":"0c89eff32c6d5b258382f21a70e2ac65dbc68a4e6d4a00f6a9152b9a6fc7b0ec","source":{"kind":"arxiv","id":"2311.03408","version":1},"attestation_state":"computed","paper":{"title":"Training Multi-layer Neural Networks on Ising Machine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE","quant-ph"],"primary_cat":"cs.LG","authors_text":"Jingliang Duan, Keqiang Li, Shengbo Eben Li, Tong Liu, Wenxuan Wang, Xujie Song","submitted_at":"2023-11-06T04:09:15Z","abstract_excerpt":"As a dedicated quantum device, Ising machines could solve large-scale binary optimization problems in milliseconds. There is emerging interest in utilizing Ising machines to train feedforward neural networks due to the prosperity of generative artificial intelligence. However, existing methods can only train single-layer feedforward networks because of the complex nonlinear network topology. This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction o"},"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":"2311.03408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T04:09:15Z","cross_cats_sorted":["cs.AI","cs.NE","quant-ph"],"title_canon_sha256":"3e63d30c423889cf1bb87acf48238973d58302d8bea10a493c6050cb06f3f4fb","abstract_canon_sha256":"7aafa41ab37b7494362112e50d1edf1ea4d4323ecd1c40ced1e765750e322e1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:57.871802Z","signature_b64":"oAsG1MJ2q31OHFquYpRKn4j1tEiCcUyx/WGCMtjhoeM+rT33gXI6R8HW1zvGWeeidyZ0LN4+OcYQrSU2U+QmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c89eff32c6d5b258382f21a70e2ac65dbc68a4e6d4a00f6a9152b9a6fc7b0ec","last_reissued_at":"2026-07-05T07:09:57.871254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:57.871254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Training Multi-layer Neural Networks on Ising Machine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE","quant-ph"],"primary_cat":"cs.LG","authors_text":"Jingliang Duan, Keqiang Li, Shengbo Eben Li, Tong Liu, Wenxuan Wang, Xujie Song","submitted_at":"2023-11-06T04:09:15Z","abstract_excerpt":"As a dedicated quantum device, Ising machines could solve large-scale binary optimization problems in milliseconds. There is emerging interest in utilizing Ising machines to train feedforward neural networks due to the prosperity of generative artificial intelligence. However, existing methods can only train single-layer feedforward networks because of the complex nonlinear network topology. This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.03408","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/2311.03408/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":"2311.03408","created_at":"2026-07-05T07:09:57.871313+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.03408v1","created_at":"2026-07-05T07:09:57.871313+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.03408","created_at":"2026-07-05T07:09:57.871313+00:00"},{"alias_kind":"pith_short_12","alias_value":"BSE674ZMNVNS","created_at":"2026-07-05T07:09:57.871313+00:00"},{"alias_kind":"pith_short_16","alias_value":"BSE674ZMNVNSLA4C","created_at":"2026-07-05T07:09:57.871313+00:00"},{"alias_kind":"pith_short_8","alias_value":"BSE674ZM","created_at":"2026-07-05T07:09:57.871313+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09117","citing_title":"Optimizing Energy-based Neural Network Training with Coherent Ising Machine","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX","json":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX.json","graph_json":"https://pith.science/api/pith-number/BSE674ZMNVNSLA4C6INHBYVMMX/graph.json","events_json":"https://pith.science/api/pith-number/BSE674ZMNVNSLA4C6INHBYVMMX/events.json","paper":"https://pith.science/paper/BSE674ZM"},"agent_actions":{"view_html":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX","download_json":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX.json","view_paper":"https://pith.science/paper/BSE674ZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.03408&json=true","fetch_graph":"https://pith.science/api/pith-number/BSE674ZMNVNSLA4C6INHBYVMMX/graph.json","fetch_events":"https://pith.science/api/pith-number/BSE674ZMNVNSLA4C6INHBYVMMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX/action/storage_attestation","attest_author":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX/action/author_attestation","sign_citation":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX/action/citation_signature","submit_replication":"https://pith.science/pith/BSE674ZMNVNSLA4C6INHBYVMMX/action/replication_record"}},"created_at":"2026-07-05T07:09:57.871313+00:00","updated_at":"2026-07-05T07:09:57.871313+00:00"}