{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IRQNH7O5MDTPWK5N3KM3OBZ6PM","short_pith_number":"pith:IRQNH7O5","schema_version":"1.0","canonical_sha256":"4460d3fddd60e6fb2badda99b7073e7b0c937a8ddaac38174b639de81578a05e","source":{"kind":"arxiv","id":"2605.21346","version":1},"attestation_state":"computed","paper":{"title":"Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Evert van Nieuwenburg, Jan A. Krzywda, Onur Danaci, Riccardo Molteni, Vedran Dunjko, Yash J. Patel","submitted_at":"2026-05-20T16:12:33Z","abstract_excerpt":"Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and under noiseless conditions, coherent processing of quantum data outperforms fixed-measurement schemes followed by classical processing. It remained uncertain whether this performance gap persists at a finite scale, and in the presence of noise that is unavoidable with current quantum devices. In this work, we present simulations and analysis of the performance of existing hardware on a learning problem known to exhibit a"},"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":"2605.21346","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-05-20T16:12:33Z","cross_cats_sorted":[],"title_canon_sha256":"8b58c0443c8293f5ca0ce83823003596e9dc177cf1c3bcc9e87a0a92b709cf96","abstract_canon_sha256":"180e40efad19b47982d7fd037cede12d4c5141ca736307cc7e05bd2b48e7901c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T02:05:30.368826Z","signature_b64":"JCDsa+rj7jewHvefEdR+1zf9kRZZy3omNOHpAhtoB5msRrfqKdzDzeD8fdEQOY7fNI4JxPdkxjz77ao5EU22Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4460d3fddd60e6fb2badda99b7073e7b0c937a8ddaac38174b639de81578a05e","last_reissued_at":"2026-05-21T02:05:30.368211Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T02:05:30.368211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Evert van Nieuwenburg, Jan A. Krzywda, Onur Danaci, Riccardo Molteni, Vedran Dunjko, Yash J. Patel","submitted_at":"2026-05-20T16:12:33Z","abstract_excerpt":"Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and under noiseless conditions, coherent processing of quantum data outperforms fixed-measurement schemes followed by classical processing. It remained uncertain whether this performance gap persists at a finite scale, and in the presence of noise that is unavoidable with current quantum devices. In this work, we present simulations and analysis of the performance of existing hardware on a learning problem known to exhibit a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21346","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/2605.21346/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":"2605.21346","created_at":"2026-05-21T02:05:30.368332+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21346v1","created_at":"2026-05-21T02:05:30.368332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21346","created_at":"2026-05-21T02:05:30.368332+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRQNH7O5MDTP","created_at":"2026-05-21T02:05:30.368332+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRQNH7O5MDTPWK5N","created_at":"2026-05-21T02:05:30.368332+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRQNH7O5","created_at":"2026-05-21T02:05:30.368332+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/IRQNH7O5MDTPWK5N3KM3OBZ6PM","json":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM.json","graph_json":"https://pith.science/api/pith-number/IRQNH7O5MDTPWK5N3KM3OBZ6PM/graph.json","events_json":"https://pith.science/api/pith-number/IRQNH7O5MDTPWK5N3KM3OBZ6PM/events.json","paper":"https://pith.science/paper/IRQNH7O5"},"agent_actions":{"view_html":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM","download_json":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM.json","view_paper":"https://pith.science/paper/IRQNH7O5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21346&json=true","fetch_graph":"https://pith.science/api/pith-number/IRQNH7O5MDTPWK5N3KM3OBZ6PM/graph.json","fetch_events":"https://pith.science/api/pith-number/IRQNH7O5MDTPWK5N3KM3OBZ6PM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM/action/storage_attestation","attest_author":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM/action/author_attestation","sign_citation":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM/action/citation_signature","submit_replication":"https://pith.science/pith/IRQNH7O5MDTPWK5N3KM3OBZ6PM/action/replication_record"}},"created_at":"2026-05-21T02:05:30.368332+00:00","updated_at":"2026-05-21T02:05:30.368332+00:00"}