{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UIM6YT4XHVJGGXHWSFOJ62YS67","short_pith_number":"pith:UIM6YT4X","schema_version":"1.0","canonical_sha256":"a219ec4f973d52635cf6915c9f6b12f7fc0ea06aa4f1981c0e895d1365109a55","source":{"kind":"arxiv","id":"2211.08567","version":2},"attestation_state":"computed","paper":{"title":"Quantum Reservoir Computing Implementations for Classical and Quantum Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Adam Burgess, Marian Florescu","submitted_at":"2022-11-15T23:19:26Z","abstract_excerpt":"Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities.\n  Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quant"},"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":"2211.08567","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-11-15T23:19:26Z","cross_cats_sorted":[],"title_canon_sha256":"633c165525d0767cfb0064d676189246d0520870bbe1df41a4fc4043bb767b62","abstract_canon_sha256":"17185d39507e3049c908414f23f79e2c046a4ecef5ba3c75b2737318f13da820"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:31.264353Z","signature_b64":"vXMQNUAgwszgRB4FsTJJ+I9S0/9K/LnR74NL7JysO93v1FTSiF0JaW8c+dN+KZskRBvDOYF+d1RDEBXEU+Y3AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a219ec4f973d52635cf6915c9f6b12f7fc0ea06aa4f1981c0e895d1365109a55","last_reissued_at":"2026-07-05T12:02:31.263817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:31.263817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Reservoir Computing Implementations for Classical and Quantum Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Adam Burgess, Marian Florescu","submitted_at":"2022-11-15T23:19:26Z","abstract_excerpt":"Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities.\n  Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.08567","kind":"arxiv","version":2},"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/2211.08567/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":"2211.08567","created_at":"2026-07-05T12:02:31.263881+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.08567v2","created_at":"2026-07-05T12:02:31.263881+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.08567","created_at":"2026-07-05T12:02:31.263881+00:00"},{"alias_kind":"pith_short_12","alias_value":"UIM6YT4XHVJG","created_at":"2026-07-05T12:02:31.263881+00:00"},{"alias_kind":"pith_short_16","alias_value":"UIM6YT4XHVJGGXHW","created_at":"2026-07-05T12:02:31.263881+00:00"},{"alias_kind":"pith_short_8","alias_value":"UIM6YT4X","created_at":"2026-07-05T12:02:31.263881+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.13878","citing_title":"Benchmarking Quantum Models for Time-series Forecasting","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67","json":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67.json","graph_json":"https://pith.science/api/pith-number/UIM6YT4XHVJGGXHWSFOJ62YS67/graph.json","events_json":"https://pith.science/api/pith-number/UIM6YT4XHVJGGXHWSFOJ62YS67/events.json","paper":"https://pith.science/paper/UIM6YT4X"},"agent_actions":{"view_html":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67","download_json":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67.json","view_paper":"https://pith.science/paper/UIM6YT4X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.08567&json=true","fetch_graph":"https://pith.science/api/pith-number/UIM6YT4XHVJGGXHWSFOJ62YS67/graph.json","fetch_events":"https://pith.science/api/pith-number/UIM6YT4XHVJGGXHWSFOJ62YS67/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67/action/storage_attestation","attest_author":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67/action/author_attestation","sign_citation":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67/action/citation_signature","submit_replication":"https://pith.science/pith/UIM6YT4XHVJGGXHWSFOJ62YS67/action/replication_record"}},"created_at":"2026-07-05T12:02:31.263881+00:00","updated_at":"2026-07-05T12:02:31.263881+00:00"}