{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RHPI6G5XQ7EBCNQOCTW4NTWW3L","short_pith_number":"pith:RHPI6G5X","schema_version":"1.0","canonical_sha256":"89de8f1bb787c811360e14edc6ced6daccb2d771436ad40298cc5eeac4fbdf93","source":{"kind":"arxiv","id":"1908.06655","version":1},"attestation_state":"computed","paper":{"title":"Quantum Expectation-Maximization Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"quant-ph","authors_text":"Hideyuki Miyahara, Kazuyuki Aihara, Wolfgang Lechner","submitted_at":"2019-08-19T09:19:54Z","abstract_excerpt":"Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the k-means algorithm has been proposed by Kerenidis, Landman, Luongo and Prakash. Based on their work, we propose a quantum expectation-maximization (EM) algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm is demonstrated. We also show numerically the advantage of GMM over k-means for non-trivial cluster data."},"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":"1908.06655","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-08-19T09:19:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b03ee4120f1399a4debd6dacfa4946427c858a4aefd2e0c987dae950ae09ecde","abstract_canon_sha256":"886087b8d5878fab35814c293ec214ea88423b8b09f827b607c1976b4fb56e16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:35:01.736622Z","signature_b64":"WCuCXMzk2IDC+EEzeJ4+xo4cyxS8+TDXPFnyLz6kW6mRcYTPSJCZHt4O/xXHXolxVpMwjbC0fcRTNKDTKqZwBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89de8f1bb787c811360e14edc6ced6daccb2d771436ad40298cc5eeac4fbdf93","last_reissued_at":"2026-07-05T00:35:01.735833Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:35:01.735833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Expectation-Maximization Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"quant-ph","authors_text":"Hideyuki Miyahara, Kazuyuki Aihara, Wolfgang Lechner","submitted_at":"2019-08-19T09:19:54Z","abstract_excerpt":"Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the k-means algorithm has been proposed by Kerenidis, Landman, Luongo and Prakash. Based on their work, we propose a quantum expectation-maximization (EM) algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm is demonstrated. We also show numerically the advantage of GMM over k-means for non-trivial cluster data."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06655","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/1908.06655/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":"1908.06655","created_at":"2026-07-05T00:35:01.735919+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.06655v1","created_at":"2026-07-05T00:35:01.735919+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06655","created_at":"2026-07-05T00:35:01.735919+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHPI6G5XQ7EB","created_at":"2026-07-05T00:35:01.735919+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHPI6G5XQ7EBCNQO","created_at":"2026-07-05T00:35:01.735919+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHPI6G5X","created_at":"2026-07-05T00:35:01.735919+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/RHPI6G5XQ7EBCNQOCTW4NTWW3L","json":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L.json","graph_json":"https://pith.science/api/pith-number/RHPI6G5XQ7EBCNQOCTW4NTWW3L/graph.json","events_json":"https://pith.science/api/pith-number/RHPI6G5XQ7EBCNQOCTW4NTWW3L/events.json","paper":"https://pith.science/paper/RHPI6G5X"},"agent_actions":{"view_html":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L","download_json":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L.json","view_paper":"https://pith.science/paper/RHPI6G5X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.06655&json=true","fetch_graph":"https://pith.science/api/pith-number/RHPI6G5XQ7EBCNQOCTW4NTWW3L/graph.json","fetch_events":"https://pith.science/api/pith-number/RHPI6G5XQ7EBCNQOCTW4NTWW3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L/action/storage_attestation","attest_author":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L/action/author_attestation","sign_citation":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L/action/citation_signature","submit_replication":"https://pith.science/pith/RHPI6G5XQ7EBCNQOCTW4NTWW3L/action/replication_record"}},"created_at":"2026-07-05T00:35:01.735919+00:00","updated_at":"2026-07-05T00:35:01.735919+00:00"}