{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:R3JCQWYX7KBSE7OJFDBLHVK4S6","short_pith_number":"pith:R3JCQWYX","schema_version":"1.0","canonical_sha256":"8ed2285b17fa83227dc928c2b3d55c978c682e82cc49d8abd2c903e16a20bc6a","source":{"kind":"arxiv","id":"1806.10910","version":1},"attestation_state":"computed","paper":{"title":"Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Keisuke Fujii, Kohei Nakajima, Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa","submitted_at":"2018-06-28T12:16:13Z","abstract_excerpt":"We experimentally demonstrate quantum machine learning using NMR based on a framework of quantum reservoir computing. Reservoir computing is for exploiting natural nonlinear dynamics with large degrees of freedom, which is called a reservoir, for a machine learning purpose. Here we propose a concrete physical implementation of a quantum reservoir using controllable dynamics of a nuclear spin ensemble in a molecular solid. In this implementation, we demonstrate learning of nonlinear functions with binary or continuous variable inputs with low mean squared errors. Our implementation and demonstr"},"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":"1806.10910","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2018-06-28T12:16:13Z","cross_cats_sorted":[],"title_canon_sha256":"b81e6f8743d842993390cc7b53584c814fd9d741fbc3d4c3cb1346e6724af202","abstract_canon_sha256":"3f2ffb55a3b11e6978b0f3192313b3db727ca8d1a21661f21dafbb196db49e26"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:07.632549Z","signature_b64":"1piO4q6ttjwShFeR3loTc1sZvMkX6sI8gqLXzJLKoNFhpYmw0iBn2lLLahcWagnalch4XFgw7AJii0nHV5UDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ed2285b17fa83227dc928c2b3d55c978c682e82cc49d8abd2c903e16a20bc6a","last_reissued_at":"2026-05-18T00:12:07.631909Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:07.631909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Keisuke Fujii, Kohei Nakajima, Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa","submitted_at":"2018-06-28T12:16:13Z","abstract_excerpt":"We experimentally demonstrate quantum machine learning using NMR based on a framework of quantum reservoir computing. Reservoir computing is for exploiting natural nonlinear dynamics with large degrees of freedom, which is called a reservoir, for a machine learning purpose. Here we propose a concrete physical implementation of a quantum reservoir using controllable dynamics of a nuclear spin ensemble in a molecular solid. In this implementation, we demonstrate learning of nonlinear functions with binary or continuous variable inputs with low mean squared errors. Our implementation and demonstr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10910","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":""},"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":"1806.10910","created_at":"2026-05-18T00:12:07.632006+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.10910v1","created_at":"2026-05-18T00:12:07.632006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10910","created_at":"2026-05-18T00:12:07.632006+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3JCQWYX7KBS","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3JCQWYX7KBSE7OJ","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3JCQWYX","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.03979","citing_title":"Harnessing quantum back-action for time-series processing","ref_index":57,"is_internal_anchor":true},{"citing_arxiv_id":"2604.03469","citing_title":"Recurrent Quantum Feature Maps for Reservoir Computing","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10471","citing_title":"Quantum and classical processing with photonic quantum machine learning","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12441","citing_title":"Efficient classical training of model-free quantum photonic reservoir","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6","json":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6.json","graph_json":"https://pith.science/api/pith-number/R3JCQWYX7KBSE7OJFDBLHVK4S6/graph.json","events_json":"https://pith.science/api/pith-number/R3JCQWYX7KBSE7OJFDBLHVK4S6/events.json","paper":"https://pith.science/paper/R3JCQWYX"},"agent_actions":{"view_html":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6","download_json":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6.json","view_paper":"https://pith.science/paper/R3JCQWYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.10910&json=true","fetch_graph":"https://pith.science/api/pith-number/R3JCQWYX7KBSE7OJFDBLHVK4S6/graph.json","fetch_events":"https://pith.science/api/pith-number/R3JCQWYX7KBSE7OJFDBLHVK4S6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6/action/storage_attestation","attest_author":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6/action/author_attestation","sign_citation":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6/action/citation_signature","submit_replication":"https://pith.science/pith/R3JCQWYX7KBSE7OJFDBLHVK4S6/action/replication_record"}},"created_at":"2026-05-18T00:12:07.632006+00:00","updated_at":"2026-05-18T00:12:07.632006+00:00"}