{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NWOM2MQEGHNAPNXG5LGS5HGK67","short_pith_number":"pith:NWOM2MQE","schema_version":"1.0","canonical_sha256":"6d9ccd320431da07b6e6eacd2e9ccaf7d54c3b2e062c894300100a3acb0321b2","source":{"kind":"arxiv","id":"2204.12192","version":1},"attestation_state":"computed","paper":{"title":"Noisy Quantum Kernel Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.other"],"primary_cat":"quant-ph","authors_text":"Alexandre Le Boit\\'e, Cristiano Ciuti, Valentin Heyraud, Zakari Denis, Zejian Li","submitted_at":"2022-04-26T09:52:02Z","abstract_excerpt":"In the noisy intermediate-scale quantum era, an important goal is the conception of implementable algorithms that exploit the rich dynamics of quantum systems and the high dimensionality of the underlying Hilbert spaces to perform tasks while prescinding from noise-proof physical systems. An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. Here, we study how dissipation and decoherence affect their performance. We address this issue by investigating the expressivity and the generalization capacity of these models within the framework of kernel theor"},"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":"2204.12192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-04-26T09:52:02Z","cross_cats_sorted":["cond-mat.other"],"title_canon_sha256":"19f1b3a82d92948a883dc3106405712f47c8bac66fc0cfa7bf93072a050bd5da","abstract_canon_sha256":"4d7b1ea781e1c975bc9bbe8cc3784dfce3888cfe08ed01b3e83efb39c75ade7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:37.089715Z","signature_b64":"WpNgB3YjofxycSJE9/LvYP4mhuUZ1ly8pcGqQbomeitTO65lcJs80WbK0N8CzarQ1U7+fy+4U2ozAMSxRSZxDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d9ccd320431da07b6e6eacd2e9ccaf7d54c3b2e062c894300100a3acb0321b2","last_reissued_at":"2026-07-05T05:20:37.088974Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:37.088974Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Noisy Quantum Kernel Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.other"],"primary_cat":"quant-ph","authors_text":"Alexandre Le Boit\\'e, Cristiano Ciuti, Valentin Heyraud, Zakari Denis, Zejian Li","submitted_at":"2022-04-26T09:52:02Z","abstract_excerpt":"In the noisy intermediate-scale quantum era, an important goal is the conception of implementable algorithms that exploit the rich dynamics of quantum systems and the high dimensionality of the underlying Hilbert spaces to perform tasks while prescinding from noise-proof physical systems. An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. Here, we study how dissipation and decoherence affect their performance. We address this issue by investigating the expressivity and the generalization capacity of these models within the framework of kernel theor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.12192","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/2204.12192/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":"2204.12192","created_at":"2026-07-05T05:20:37.089078+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.12192v1","created_at":"2026-07-05T05:20:37.089078+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.12192","created_at":"2026-07-05T05:20:37.089078+00:00"},{"alias_kind":"pith_short_12","alias_value":"NWOM2MQEGHNA","created_at":"2026-07-05T05:20:37.089078+00:00"},{"alias_kind":"pith_short_16","alias_value":"NWOM2MQEGHNAPNXG","created_at":"2026-07-05T05:20:37.089078+00:00"},{"alias_kind":"pith_short_8","alias_value":"NWOM2MQE","created_at":"2026-07-05T05:20:37.089078+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21276","citing_title":"Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67","json":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67.json","graph_json":"https://pith.science/api/pith-number/NWOM2MQEGHNAPNXG5LGS5HGK67/graph.json","events_json":"https://pith.science/api/pith-number/NWOM2MQEGHNAPNXG5LGS5HGK67/events.json","paper":"https://pith.science/paper/NWOM2MQE"},"agent_actions":{"view_html":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67","download_json":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67.json","view_paper":"https://pith.science/paper/NWOM2MQE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.12192&json=true","fetch_graph":"https://pith.science/api/pith-number/NWOM2MQEGHNAPNXG5LGS5HGK67/graph.json","fetch_events":"https://pith.science/api/pith-number/NWOM2MQEGHNAPNXG5LGS5HGK67/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67/action/storage_attestation","attest_author":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67/action/author_attestation","sign_citation":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67/action/citation_signature","submit_replication":"https://pith.science/pith/NWOM2MQEGHNAPNXG5LGS5HGK67/action/replication_record"}},"created_at":"2026-07-05T05:20:37.089078+00:00","updated_at":"2026-07-05T05:20:37.089078+00:00"}