{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YNQQ22X5JUMATVHK2VJDFBABRQ","short_pith_number":"pith:YNQQ22X5","schema_version":"1.0","canonical_sha256":"c3610d6afd4d1809d4ead5523284018c023b11682c6f3d7c5d1861082f3f0b7c","source":{"kind":"arxiv","id":"2402.16465","version":1},"attestation_state":"computed","paper":{"title":"Training Classical Neural Networks by Quantum Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Chen-Yu Liu, Chu-Hsuan Abraham Lin, En-Jui Kuo, Jason Gemsun Young, Min-Hsiu Hsieh, Sean Chen, Yeong-Jar Chang","submitted_at":"2024-02-26T10:16:21Z","abstract_excerpt":"In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\\text{polylog} (M))$ rotational gate angles, we can significantly reduce the number of parameters. These gate angles can be updated to train the c"},"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":"2402.16465","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"quant-ph","submitted_at":"2024-02-26T10:16:21Z","cross_cats_sorted":[],"title_canon_sha256":"dd6ae00b3e3e78918ec8b4071d61e9c3ae1406c18612f75353712e6cb5f1c6eb","abstract_canon_sha256":"7d2cf1628738f1fee3ab0b0b980bd67f41d70f307dad26082330d5612f5e57b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:21.727559Z","signature_b64":"VYpz9+l6ePkPSJBeGMRNyzn7+4V2VtccQeGNuj+InmJb9hczCKSklRBRV9aBCn+RR9Gj19zRYHGzV8L+4NHBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3610d6afd4d1809d4ead5523284018c023b11682c6f3d7c5d1861082f3f0b7c","last_reissued_at":"2026-07-05T07:49:21.727073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:21.727073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Training Classical Neural Networks by Quantum Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Chen-Yu Liu, Chu-Hsuan Abraham Lin, En-Jui Kuo, Jason Gemsun Young, Min-Hsiu Hsieh, Sean Chen, Yeong-Jar Chang","submitted_at":"2024-02-26T10:16:21Z","abstract_excerpt":"In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\\text{polylog} (M))$ rotational gate angles, we can significantly reduce the number of parameters. These gate angles can be updated to train the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16465","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/2402.16465/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":"2402.16465","created_at":"2026-07-05T07:49:21.727131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16465v1","created_at":"2026-07-05T07:49:21.727131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16465","created_at":"2026-07-05T07:49:21.727131+00:00"},{"alias_kind":"pith_short_12","alias_value":"YNQQ22X5JUMA","created_at":"2026-07-05T07:49:21.727131+00:00"},{"alias_kind":"pith_short_16","alias_value":"YNQQ22X5JUMATVHK","created_at":"2026-07-05T07:49:21.727131+00:00"},{"alias_kind":"pith_short_8","alias_value":"YNQQ22X5","created_at":"2026-07-05T07:49:21.727131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01507","citing_title":"Transfer Learning Analysis of Variational Quantum Circuits","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ","json":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ.json","graph_json":"https://pith.science/api/pith-number/YNQQ22X5JUMATVHK2VJDFBABRQ/graph.json","events_json":"https://pith.science/api/pith-number/YNQQ22X5JUMATVHK2VJDFBABRQ/events.json","paper":"https://pith.science/paper/YNQQ22X5"},"agent_actions":{"view_html":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ","download_json":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ.json","view_paper":"https://pith.science/paper/YNQQ22X5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16465&json=true","fetch_graph":"https://pith.science/api/pith-number/YNQQ22X5JUMATVHK2VJDFBABRQ/graph.json","fetch_events":"https://pith.science/api/pith-number/YNQQ22X5JUMATVHK2VJDFBABRQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ/action/storage_attestation","attest_author":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ/action/author_attestation","sign_citation":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ/action/citation_signature","submit_replication":"https://pith.science/pith/YNQQ22X5JUMATVHK2VJDFBABRQ/action/replication_record"}},"created_at":"2026-07-05T07:49:21.727131+00:00","updated_at":"2026-07-05T07:49:21.727131+00:00"}