{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7MPW2F4TDDXGYDFNHJSDTW2HE6","short_pith_number":"pith:7MPW2F4T","schema_version":"1.0","canonical_sha256":"fb1f6d179318ee6c0cad3a6439db4727a007828e92ab5d8f131960220162b1d0","source":{"kind":"arxiv","id":"1808.09607","version":1},"attestation_state":"computed","paper":{"title":"Nonlinear regression based on a hybrid quantum computer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang","submitted_at":"2018-08-29T02:24:50Z","abstract_excerpt":"Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid quantum computer, exploiting both discrete and continuous variables, for their capacity to encode novel features and efficiency of processing information. We propose encoding schemes that can realize well-known polynomial and Gaussian kernel ridge regressions, with exponentially "},"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":"1808.09607","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2018-08-29T02:24:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"74de7ce8f6f5164d30ce455f6983a8b0a44bebf00ffb890e91580a56b064c0d9","abstract_canon_sha256":"edb4896dfda4e656735f03cf5ae668fff0f253f7798e2c9eb2059eb508cca921"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:55.023636Z","signature_b64":"NDM9vdxn/GD6oNogsOVt3LUEgde2BHeyKIsoU8nelNJfOTo5QFh0w3qUhe+Hw+ZmT9JJ4qYbUspl2oGwIVdoCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb1f6d179318ee6c0cad3a6439db4727a007828e92ab5d8f131960220162b1d0","last_reissued_at":"2026-05-18T00:06:55.023061Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:55.023061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonlinear regression based on a hybrid quantum computer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang","submitted_at":"2018-08-29T02:24:50Z","abstract_excerpt":"Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid quantum computer, exploiting both discrete and continuous variables, for their capacity to encode novel features and efficiency of processing information. We propose encoding schemes that can realize well-known polynomial and Gaussian kernel ridge regressions, with exponentially "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09607","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":"1808.09607","created_at":"2026-05-18T00:06:55.023155+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.09607v1","created_at":"2026-05-18T00:06:55.023155+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09607","created_at":"2026-05-18T00:06:55.023155+00:00"},{"alias_kind":"pith_short_12","alias_value":"7MPW2F4TDDXG","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7MPW2F4TDDXGYDFN","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7MPW2F4T","created_at":"2026-05-18T12:32:11.075285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6","json":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6.json","graph_json":"https://pith.science/api/pith-number/7MPW2F4TDDXGYDFNHJSDTW2HE6/graph.json","events_json":"https://pith.science/api/pith-number/7MPW2F4TDDXGYDFNHJSDTW2HE6/events.json","paper":"https://pith.science/paper/7MPW2F4T"},"agent_actions":{"view_html":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6","download_json":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6.json","view_paper":"https://pith.science/paper/7MPW2F4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.09607&json=true","fetch_graph":"https://pith.science/api/pith-number/7MPW2F4TDDXGYDFNHJSDTW2HE6/graph.json","fetch_events":"https://pith.science/api/pith-number/7MPW2F4TDDXGYDFNHJSDTW2HE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6/action/storage_attestation","attest_author":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6/action/author_attestation","sign_citation":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6/action/citation_signature","submit_replication":"https://pith.science/pith/7MPW2F4TDDXGYDFNHJSDTW2HE6/action/replication_record"}},"created_at":"2026-05-18T00:06:55.023155+00:00","updated_at":"2026-05-18T00:06:55.023155+00:00"}