{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5F4IXFGXPHWVX363J7JDDQCTEN","short_pith_number":"pith:5F4IXFGX","schema_version":"1.0","canonical_sha256":"e9788b94d779ed5befdb4fd231c05323568cfe6787007976c668f3f753fc6062","source":{"kind":"arxiv","id":"2301.03251","version":1},"attestation_state":"computed","paper":{"title":"VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"GuoPing Guo, Hanchao Wang, Huanyu Bian, Lei Li, Menghan Dou, Nenghai Yu, Weiming Zhang, Wei Wang, Wenyu Zhu, Yang Yang, Ye Li, Yiming Zhao, Yuan Fang, Zhaohui Zhou, Zhaoyun Chen, Zhilong Jia","submitted_at":"2023-01-09T10:31:18Z","abstract_excerpt":"With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we"},"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":"2301.03251","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2023-01-09T10:31:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ddd8047ea4edd152aded9f56c32b14aaaf8a0f42ebf1d2c574809772e1c3e1b5","abstract_canon_sha256":"d1471240df2b101b89188ee44e7c45880dbe931845be4b49fd433d029c1bf810"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:27.370692Z","signature_b64":"XR/nAqtUYzg3p0s0aj8BNN8JZxpdM3D4Y9oE9s+MSA3eFIXEkkVLU73M3OAFNhTIyWvaliLrfYtMb+G5JJ0YCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9788b94d779ed5befdb4fd231c05323568cfe6787007976c668f3f753fc6062","last_reissued_at":"2026-07-05T05:31:27.370108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:27.370108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"GuoPing Guo, Hanchao Wang, Huanyu Bian, Lei Li, Menghan Dou, Nenghai Yu, Weiming Zhang, Wei Wang, Wenyu Zhu, Yang Yang, Ye Li, Yiming Zhao, Yuan Fang, Zhaohui Zhou, Zhaoyun Chen, Zhilong Jia","submitted_at":"2023-01-09T10:31:18Z","abstract_excerpt":"With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03251","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/2301.03251/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":"2301.03251","created_at":"2026-07-05T05:31:27.370206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.03251v1","created_at":"2026-07-05T05:31:27.370206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03251","created_at":"2026-07-05T05:31:27.370206+00:00"},{"alias_kind":"pith_short_12","alias_value":"5F4IXFGXPHWV","created_at":"2026-07-05T05:31:27.370206+00:00"},{"alias_kind":"pith_short_16","alias_value":"5F4IXFGXPHWVX363","created_at":"2026-07-05T05:31:27.370206+00:00"},{"alias_kind":"pith_short_8","alias_value":"5F4IXFGX","created_at":"2026-07-05T05:31:27.370206+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN","json":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN.json","graph_json":"https://pith.science/api/pith-number/5F4IXFGXPHWVX363J7JDDQCTEN/graph.json","events_json":"https://pith.science/api/pith-number/5F4IXFGXPHWVX363J7JDDQCTEN/events.json","paper":"https://pith.science/paper/5F4IXFGX"},"agent_actions":{"view_html":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN","download_json":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN.json","view_paper":"https://pith.science/paper/5F4IXFGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.03251&json=true","fetch_graph":"https://pith.science/api/pith-number/5F4IXFGXPHWVX363J7JDDQCTEN/graph.json","fetch_events":"https://pith.science/api/pith-number/5F4IXFGXPHWVX363J7JDDQCTEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN/action/storage_attestation","attest_author":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN/action/author_attestation","sign_citation":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN/action/citation_signature","submit_replication":"https://pith.science/pith/5F4IXFGXPHWVX363J7JDDQCTEN/action/replication_record"}},"created_at":"2026-07-05T05:31:27.370206+00:00","updated_at":"2026-07-05T05:31:27.370206+00:00"}