{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:AIMW6Q25MN5HNNY5B7UPO5LZMQ","short_pith_number":"pith:AIMW6Q25","schema_version":"1.0","canonical_sha256":"02196f435d637a76b71d0fe8f77579641d1f5eb4c540ace046a12b5791658451","source":{"kind":"arxiv","id":"2112.08449","version":1},"attestation_state":"computed","paper":{"title":"Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Andrew Lockwood, Anna Phan, Annie Naveh, Imogen Fitzgerald, Travis L. Scholten","submitted_at":"2021-12-15T19:44:39Z","abstract_excerpt":"Enhancing classical machine learning (ML) algorithms through quantum kernels is a rapidly growing research topic in quantum machine learning (QML). A key challenge in using kernels -- both classical and quantum -- is that ML workflows involve acquiring new observations, for which new kernel values need to be calculated. Transferring data back-and-forth between where the new observations are generated & a quantum computer incurs a time delay; this delay may exceed the timescales relevant for using the QML algorithm in the first place. In this work, we show quantum kernel matrices can be extende"},"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":"2112.08449","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-12-15T19:44:39Z","cross_cats_sorted":[],"title_canon_sha256":"1f58d602cd512509ba1d5d806308e08f563cc9a343084bc9bb64e0a935400eb4","abstract_canon_sha256":"26ad8c6281349520370d94292dff25acfb6fbdbce5e4c6803390a040957ff169"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:29.349908Z","signature_b64":"CCiF0ccOxtCFkx5pL6EruMQyBOB7el2XIONVxU1kpdfTMr3XfZT+h4AStaa/g2W/c1qxzM6b03ogZyK/yTrYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02196f435d637a76b71d0fe8f77579641d1f5eb4c540ace046a12b5791658451","last_reissued_at":"2026-07-05T03:41:29.349500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:29.349500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Andrew Lockwood, Anna Phan, Annie Naveh, Imogen Fitzgerald, Travis L. Scholten","submitted_at":"2021-12-15T19:44:39Z","abstract_excerpt":"Enhancing classical machine learning (ML) algorithms through quantum kernels is a rapidly growing research topic in quantum machine learning (QML). A key challenge in using kernels -- both classical and quantum -- is that ML workflows involve acquiring new observations, for which new kernel values need to be calculated. Transferring data back-and-forth between where the new observations are generated & a quantum computer incurs a time delay; this delay may exceed the timescales relevant for using the QML algorithm in the first place. In this work, we show quantum kernel matrices can be extende"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08449","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/2112.08449/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":"2112.08449","created_at":"2026-07-05T03:41:29.349560+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.08449v1","created_at":"2026-07-05T03:41:29.349560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08449","created_at":"2026-07-05T03:41:29.349560+00:00"},{"alias_kind":"pith_short_12","alias_value":"AIMW6Q25MN5H","created_at":"2026-07-05T03:41:29.349560+00:00"},{"alias_kind":"pith_short_16","alias_value":"AIMW6Q25MN5HNNY5","created_at":"2026-07-05T03:41:29.349560+00:00"},{"alias_kind":"pith_short_8","alias_value":"AIMW6Q25","created_at":"2026-07-05T03:41:29.349560+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22275","citing_title":"Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ","json":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ.json","graph_json":"https://pith.science/api/pith-number/AIMW6Q25MN5HNNY5B7UPO5LZMQ/graph.json","events_json":"https://pith.science/api/pith-number/AIMW6Q25MN5HNNY5B7UPO5LZMQ/events.json","paper":"https://pith.science/paper/AIMW6Q25"},"agent_actions":{"view_html":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ","download_json":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ.json","view_paper":"https://pith.science/paper/AIMW6Q25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.08449&json=true","fetch_graph":"https://pith.science/api/pith-number/AIMW6Q25MN5HNNY5B7UPO5LZMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/AIMW6Q25MN5HNNY5B7UPO5LZMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ/action/storage_attestation","attest_author":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ/action/author_attestation","sign_citation":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ/action/citation_signature","submit_replication":"https://pith.science/pith/AIMW6Q25MN5HNNY5B7UPO5LZMQ/action/replication_record"}},"created_at":"2026-07-05T03:41:29.349560+00:00","updated_at":"2026-07-05T03:41:29.349560+00:00"}