{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MYLMO7ALG2QBR534WFVPMXQPVT","short_pith_number":"pith:MYLMO7AL","schema_version":"1.0","canonical_sha256":"6616c77c0b36a018f77cb16af65e0facd135e3d6960665f61d42fb8ced1076f4","source":{"kind":"arxiv","id":"2409.05846","version":1},"attestation_state":"computed","paper":{"title":"An Introduction to Quantum Reinforcement Learning (QRL)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG","cs.NE"],"primary_cat":"quant-ph","authors_text":"Samuel Yen-Chi Chen","submitted_at":"2024-09-09T17:45:37Z","abstract_excerpt":"Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broade"},"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":"2409.05846","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2024-09-09T17:45:37Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.NE"],"title_canon_sha256":"8d3f797b1ddf9f911afcca6400cbc9db445080b86faeb9648b1f254ef2fc689e","abstract_canon_sha256":"5a03992b320bc1a58d419537124c2cd7023a07ffe5b2cfc59bef2fa9d4bbfc3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:54.993201Z","signature_b64":"0LDEkmRWU6POvlAbQBaEgFS+/7ncyxcgj4bRc+Q87Pur7XcxhSVF0ZZI3sgEfWyIpFSSYH8SiQdbO8HOMj0qBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6616c77c0b36a018f77cb16af65e0facd135e3d6960665f61d42fb8ced1076f4","last_reissued_at":"2026-07-05T09:04:54.992734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:54.992734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Introduction to Quantum Reinforcement Learning (QRL)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG","cs.NE"],"primary_cat":"quant-ph","authors_text":"Samuel Yen-Chi Chen","submitted_at":"2024-09-09T17:45:37Z","abstract_excerpt":"Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broade"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.05846","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/2409.05846/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":"2409.05846","created_at":"2026-07-05T09:04:54.992801+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.05846v1","created_at":"2026-07-05T09:04:54.992801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.05846","created_at":"2026-07-05T09:04:54.992801+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYLMO7ALG2QB","created_at":"2026-07-05T09:04:54.992801+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYLMO7ALG2QBR534","created_at":"2026-07-05T09:04:54.992801+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYLMO7AL","created_at":"2026-07-05T09:04:54.992801+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21213","citing_title":"Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2509.16002","citing_title":"Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT","json":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT.json","graph_json":"https://pith.science/api/pith-number/MYLMO7ALG2QBR534WFVPMXQPVT/graph.json","events_json":"https://pith.science/api/pith-number/MYLMO7ALG2QBR534WFVPMXQPVT/events.json","paper":"https://pith.science/paper/MYLMO7AL"},"agent_actions":{"view_html":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT","download_json":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT.json","view_paper":"https://pith.science/paper/MYLMO7AL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.05846&json=true","fetch_graph":"https://pith.science/api/pith-number/MYLMO7ALG2QBR534WFVPMXQPVT/graph.json","fetch_events":"https://pith.science/api/pith-number/MYLMO7ALG2QBR534WFVPMXQPVT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT/action/storage_attestation","attest_author":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT/action/author_attestation","sign_citation":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT/action/citation_signature","submit_replication":"https://pith.science/pith/MYLMO7ALG2QBR534WFVPMXQPVT/action/replication_record"}},"created_at":"2026-07-05T09:04:54.992801+00:00","updated_at":"2026-07-05T09:04:54.992801+00:00"}