{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PPAWQFQJI64D2EU7F66W7QAYFR","short_pith_number":"pith:PPAWQFQJ","schema_version":"1.0","canonical_sha256":"7bc168160947b83d129f2fbd6fc0182c44d51b0bef63f125a6efdf8fc35ccd85","source":{"kind":"arxiv","id":"2311.08990","version":2},"attestation_state":"computed","paper":{"title":"sQUlearn -- A Python Library for Quantum Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"David A. Kreplin, Frederic Rapp, Jan Schnabel, Manuel Hagel\\\"uken, Marco Roth, Moritz Willmann","submitted_at":"2023-11-15T14:22:53Z","abstract_excerpt":"sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By "},"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":"2311.08990","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2023-11-15T14:22:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b109e2558de09bdf3ba340a118c335cb873e260df35b00570d10479aa841b92b","abstract_canon_sha256":"31594e8c37f02bab3ee97ab01224853833dbf4ace523f3f59668e8e4bb4b5ffd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:36.072605Z","signature_b64":"lYN5+WWdLIis822sD8YK5G/8IW3DEiKNMhjNG3zrLF3wv3gGGpPlPmTk469F5cGeXv5oVsX/mubYt2evo403CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bc168160947b83d129f2fbd6fc0182c44d51b0bef63f125a6efdf8fc35ccd85","last_reissued_at":"2026-07-05T09:25:36.072063Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:36.072063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"sQUlearn -- A Python Library for Quantum Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"David A. Kreplin, Frederic Rapp, Jan Schnabel, Manuel Hagel\\\"uken, Marco Roth, Moritz Willmann","submitted_at":"2023-11-15T14:22:53Z","abstract_excerpt":"sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08990","kind":"arxiv","version":2},"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/2311.08990/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":"2311.08990","created_at":"2026-07-05T09:25:36.072125+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.08990v2","created_at":"2026-07-05T09:25:36.072125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08990","created_at":"2026-07-05T09:25:36.072125+00:00"},{"alias_kind":"pith_short_12","alias_value":"PPAWQFQJI64D","created_at":"2026-07-05T09:25:36.072125+00:00"},{"alias_kind":"pith_short_16","alias_value":"PPAWQFQJI64D2EU7","created_at":"2026-07-05T09:25:36.072125+00:00"},{"alias_kind":"pith_short_8","alias_value":"PPAWQFQJ","created_at":"2026-07-05T09:25:36.072125+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.00504","citing_title":"Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$","ref_index":2024,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR","json":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR.json","graph_json":"https://pith.science/api/pith-number/PPAWQFQJI64D2EU7F66W7QAYFR/graph.json","events_json":"https://pith.science/api/pith-number/PPAWQFQJI64D2EU7F66W7QAYFR/events.json","paper":"https://pith.science/paper/PPAWQFQJ"},"agent_actions":{"view_html":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR","download_json":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR.json","view_paper":"https://pith.science/paper/PPAWQFQJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.08990&json=true","fetch_graph":"https://pith.science/api/pith-number/PPAWQFQJI64D2EU7F66W7QAYFR/graph.json","fetch_events":"https://pith.science/api/pith-number/PPAWQFQJI64D2EU7F66W7QAYFR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR/action/storage_attestation","attest_author":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR/action/author_attestation","sign_citation":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR/action/citation_signature","submit_replication":"https://pith.science/pith/PPAWQFQJI64D2EU7F66W7QAYFR/action/replication_record"}},"created_at":"2026-07-05T09:25:36.072125+00:00","updated_at":"2026-07-05T09:25:36.072125+00:00"}