{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G2GC3D6SGPJ4QWH6XR5R7QVS6I","short_pith_number":"pith:G2GC3D6S","schema_version":"1.0","canonical_sha256":"368c2d8fd233d3c858febc7b1fc2b2f221e10dd25294ccd6ea9636766f2e498a","source":{"kind":"arxiv","id":"2311.14105","version":2},"attestation_state":"computed","paper":{"title":"Hybrid quantum-classical reservoir computing for simulating chaotic systems","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Ata Akbari Asanjan, Daniel O`Connor, Davide Venturelli, Elena Strbac, Filip Wudarski, Max Wilson, P. Aaron Lott, Shaun Geaney","submitted_at":"2023-11-23T17:07:02Z","abstract_excerpt":"Forecasting chaotic systems is a notably complex task, which in recent years has been approached with reasonable success using reservoir computing (RC), a recurrent network with fixed random weights (the reservoir) used to extract the spatio-temporal information of the system. This work presents a hybrid quantum reservoir-computing (HQRC) framework, which replaces the reservoir in RC with a quantum circuit. The modular structure and measurement feedback in the circuit are used to encode the complex system dynamics in the reservoir states, from which classical learning is performed to predict f"},"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":"2311.14105","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"quant-ph","submitted_at":"2023-11-23T17:07:02Z","cross_cats_sorted":[],"title_canon_sha256":"fe51630d33d053028afd95fa9020583046f9e31279578bcc565d0ed40e1359fd","abstract_canon_sha256":"fd9fcc2b37942c568621067f50d24e9ca279b06990abb83989867e8cb2e962f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:42.143356Z","signature_b64":"cXISJ08dzZuOUi8anWwUVYmFs1p+olaq1o/LTxilZWeCKvtaNvBu/WLakg2XNQY72LnY3P19Zn3sh64pNjavDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"368c2d8fd233d3c858febc7b1fc2b2f221e10dd25294ccd6ea9636766f2e498a","last_reissued_at":"2026-07-05T08:11:42.142879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:42.142879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid quantum-classical reservoir computing for simulating chaotic systems","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Ata Akbari Asanjan, Daniel O`Connor, Davide Venturelli, Elena Strbac, Filip Wudarski, Max Wilson, P. Aaron Lott, Shaun Geaney","submitted_at":"2023-11-23T17:07:02Z","abstract_excerpt":"Forecasting chaotic systems is a notably complex task, which in recent years has been approached with reasonable success using reservoir computing (RC), a recurrent network with fixed random weights (the reservoir) used to extract the spatio-temporal information of the system. This work presents a hybrid quantum reservoir-computing (HQRC) framework, which replaces the reservoir in RC with a quantum circuit. The modular structure and measurement feedback in the circuit are used to encode the complex system dynamics in the reservoir states, from which classical learning is performed to predict f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.14105","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/2311.14105/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":"2311.14105","created_at":"2026-07-05T08:11:42.142936+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.14105v2","created_at":"2026-07-05T08:11:42.142936+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.14105","created_at":"2026-07-05T08:11:42.142936+00:00"},{"alias_kind":"pith_short_12","alias_value":"G2GC3D6SGPJ4","created_at":"2026-07-05T08:11:42.142936+00:00"},{"alias_kind":"pith_short_16","alias_value":"G2GC3D6SGPJ4QWH6","created_at":"2026-07-05T08:11:42.142936+00:00"},{"alias_kind":"pith_short_8","alias_value":"G2GC3D6S","created_at":"2026-07-05T08:11:42.142936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21327","citing_title":"Temporal processing of quantum states with hybrid quantum-classical reservoirs","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2411.03979","citing_title":"Harnessing quantum back-action for time-series processing","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23743","citing_title":"An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I","json":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I.json","graph_json":"https://pith.science/api/pith-number/G2GC3D6SGPJ4QWH6XR5R7QVS6I/graph.json","events_json":"https://pith.science/api/pith-number/G2GC3D6SGPJ4QWH6XR5R7QVS6I/events.json","paper":"https://pith.science/paper/G2GC3D6S"},"agent_actions":{"view_html":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I","download_json":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I.json","view_paper":"https://pith.science/paper/G2GC3D6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.14105&json=true","fetch_graph":"https://pith.science/api/pith-number/G2GC3D6SGPJ4QWH6XR5R7QVS6I/graph.json","fetch_events":"https://pith.science/api/pith-number/G2GC3D6SGPJ4QWH6XR5R7QVS6I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I/action/storage_attestation","attest_author":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I/action/author_attestation","sign_citation":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I/action/citation_signature","submit_replication":"https://pith.science/pith/G2GC3D6SGPJ4QWH6XR5R7QVS6I/action/replication_record"}},"created_at":"2026-07-05T08:11:42.142936+00:00","updated_at":"2026-07-05T08:11:42.142936+00:00"}