{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:Y2QM4UAOVHGD7S5TXFKRMW47JB","short_pith_number":"pith:Y2QM4UAO","schema_version":"1.0","canonical_sha256":"c6a0ce500ea9cc3fcbb3b955165b9f4841796a1d6f2b7f596aed9fc1f05abad3","source":{"kind":"arxiv","id":"2003.14103","version":1},"attestation_state":"computed","paper":{"title":"No Free Lunch for Quantum Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Kerstin Beer, Kyle Poland, Tobias J. Osborne","submitted_at":"2020-03-31T11:19:41Z","abstract_excerpt":"The ultimate limits for the quantum machine learning of quantum data are investigated by obtaining a generalisation of the celebrated No Free Lunch (NFL) theorem. We find a lower bound on the quantum risk (the probability that a trained hypothesis is incorrect when presented with a random input) of a quantum learning algorithm trained via pairs of input and output states when averaged over training pairs and unitaries. The bound is illustrated using a recently introduced QNN architecture."},"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":"2003.14103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2020-03-31T11:19:41Z","cross_cats_sorted":[],"title_canon_sha256":"79819acba43d511460af1ddf4765942a44f56148d314ab6f8e47ee0f5690cec5","abstract_canon_sha256":"28818715fd26bac02b052950b537e675cfd9b989cd1d4ee5aaca6facc413b445"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:51:47.929993Z","signature_b64":"bTdb0/wOVbpBJU7jMsUmm7mbPaNd0r87lKCyjOpYch8Se6hyhbyPJE2/uId/2JeaA3V1VXiDM1qfX4m4zu3xAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6a0ce500ea9cc3fcbb3b955165b9f4841796a1d6f2b7f596aed9fc1f05abad3","last_reissued_at":"2026-07-05T00:51:47.929628Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:51:47.929628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"No Free Lunch for Quantum Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Kerstin Beer, Kyle Poland, Tobias J. Osborne","submitted_at":"2020-03-31T11:19:41Z","abstract_excerpt":"The ultimate limits for the quantum machine learning of quantum data are investigated by obtaining a generalisation of the celebrated No Free Lunch (NFL) theorem. We find a lower bound on the quantum risk (the probability that a trained hypothesis is incorrect when presented with a random input) of a quantum learning algorithm trained via pairs of input and output states when averaged over training pairs and unitaries. The bound is illustrated using a recently introduced QNN architecture."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.14103","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/2003.14103/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":"2003.14103","created_at":"2026-07-05T00:51:47.929683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.14103v1","created_at":"2026-07-05T00:51:47.929683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.14103","created_at":"2026-07-05T00:51:47.929683+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2QM4UAOVHGD","created_at":"2026-07-05T00:51:47.929683+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2QM4UAOVHGD7S5T","created_at":"2026-07-05T00:51:47.929683+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2QM4UAO","created_at":"2026-07-05T00:51:47.929683+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22331","citing_title":"No Reference-Free Generalization in Quantum Machine Learning","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2303.12834","citing_title":"The power and limitations of learning quantum dynamics incoherently","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2405.08319","citing_title":"Measurement-based quantum machine learning","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB","json":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB.json","graph_json":"https://pith.science/api/pith-number/Y2QM4UAOVHGD7S5TXFKRMW47JB/graph.json","events_json":"https://pith.science/api/pith-number/Y2QM4UAOVHGD7S5TXFKRMW47JB/events.json","paper":"https://pith.science/paper/Y2QM4UAO"},"agent_actions":{"view_html":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB","download_json":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB.json","view_paper":"https://pith.science/paper/Y2QM4UAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.14103&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2QM4UAOVHGD7S5TXFKRMW47JB/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2QM4UAOVHGD7S5TXFKRMW47JB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB/action/storage_attestation","attest_author":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB/action/author_attestation","sign_citation":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB/action/citation_signature","submit_replication":"https://pith.science/pith/Y2QM4UAOVHGD7S5TXFKRMW47JB/action/replication_record"}},"created_at":"2026-07-05T00:51:47.929683+00:00","updated_at":"2026-07-05T00:51:47.929683+00:00"}