{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:B7GJPHBMOD7JTMLO2W2M27C4QJ","short_pith_number":"pith:B7GJPHBM","schema_version":"1.0","canonical_sha256":"0fcc979c2c70fe99b16ed5b4cd7c5c82697c3ca81e93dbdfc3487876d38b250c","source":{"kind":"arxiv","id":"2302.03244","version":1},"attestation_state":"computed","paper":{"title":"Quantum Recurrent Neural Networks for Sequential Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Guoqiang Zhong, Haiyong Zheng, Jiaxin Li, Rongbing Han, Ruimin Shang, Shangshang Shi, Yanan Li, YongJian Gu, Zhimin Wang","submitted_at":"2023-02-07T04:04:39Z","abstract_excerpt":"Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent"},"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":"2302.03244","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2023-02-07T04:04:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"47ec1e3d6ec3239eb8c8b3b472b2307197ca9d019786fec1fbf7faa05c5fe7f2","abstract_canon_sha256":"1fbabc228cfc05bab2beaa8f457c06c1f237aceb8db2db1cecf568f9c2a6c883"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:35.353674Z","signature_b64":"IM1NnBsw/yFhbMPgsePsJkFv2PXkf9A36fX02m1K3uYl0VlWzpqmEfs+tx7PJNW9TpNZE6Z/UhtlQjPk8GzSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fcc979c2c70fe99b16ed5b4cd7c5c82697c3ca81e93dbdfc3487876d38b250c","last_reissued_at":"2026-07-05T05:39:35.353132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:35.353132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Recurrent Neural Networks for Sequential Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Guoqiang Zhong, Haiyong Zheng, Jiaxin Li, Rongbing Han, Ruimin Shang, Shangshang Shi, Yanan Li, YongJian Gu, Zhimin Wang","submitted_at":"2023-02-07T04:04:39Z","abstract_excerpt":"Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03244","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/2302.03244/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":"2302.03244","created_at":"2026-07-05T05:39:35.353193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.03244v1","created_at":"2026-07-05T05:39:35.353193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03244","created_at":"2026-07-05T05:39:35.353193+00:00"},{"alias_kind":"pith_short_12","alias_value":"B7GJPHBMOD7J","created_at":"2026-07-05T05:39:35.353193+00:00"},{"alias_kind":"pith_short_16","alias_value":"B7GJPHBMOD7JTMLO","created_at":"2026-07-05T05:39:35.353193+00:00"},{"alias_kind":"pith_short_8","alias_value":"B7GJPHBM","created_at":"2026-07-05T05:39:35.353193+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.11092","citing_title":"MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ","json":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ.json","graph_json":"https://pith.science/api/pith-number/B7GJPHBMOD7JTMLO2W2M27C4QJ/graph.json","events_json":"https://pith.science/api/pith-number/B7GJPHBMOD7JTMLO2W2M27C4QJ/events.json","paper":"https://pith.science/paper/B7GJPHBM"},"agent_actions":{"view_html":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ","download_json":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ.json","view_paper":"https://pith.science/paper/B7GJPHBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.03244&json=true","fetch_graph":"https://pith.science/api/pith-number/B7GJPHBMOD7JTMLO2W2M27C4QJ/graph.json","fetch_events":"https://pith.science/api/pith-number/B7GJPHBMOD7JTMLO2W2M27C4QJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ/action/storage_attestation","attest_author":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ/action/author_attestation","sign_citation":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ/action/citation_signature","submit_replication":"https://pith.science/pith/B7GJPHBMOD7JTMLO2W2M27C4QJ/action/replication_record"}},"created_at":"2026-07-05T05:39:35.353193+00:00","updated_at":"2026-07-05T05:39:35.353193+00:00"}