{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AEUSMEIXX3BBDQL4MCN3Y7MRTI","short_pith_number":"pith:AEUSMEIX","schema_version":"1.0","canonical_sha256":"0129261117bec211c17c609bbc7d919a3640cac289c7ee5d0b4a55e1ce7b64ce","source":{"kind":"arxiv","id":"1804.03546","version":3},"attestation_state":"computed","paper":{"title":"Machine learning algorithms based on generalized Gibbs ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-th","physics.comp-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"Axel Cortes Cubero, Tatjana Puskarov","submitted_at":"2018-04-10T14:04:18Z","abstract_excerpt":"Machine learning algorithms often take inspiration from established results and knowledge from statistical physics. A prototypical example is the Boltzmann machine algorithm for supervised learning, which utilizes knowledge of classical thermal partition functions and the Boltzmann distribution. Recently, a quantum version of the Boltzmann machine was introduced by Amin, et. al., however, non-commutativity of quantum operators renders the training process by minimizing a cost function inefficient. Recent advances in the study of non-equilibrium quantum integrable systems, which never thermaliz"},"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":"1804.03546","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2018-04-10T14:04:18Z","cross_cats_sorted":["hep-th","physics.comp-ph"],"title_canon_sha256":"d28b5492b9aa7931796ab40863b377fc38946ede3e89e6aa53482044502cf97c","abstract_canon_sha256":"db92a32d5ce4062a9fb35d54324af123633ceabe899b9f31fdd3d63657ad653e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:22.827613Z","signature_b64":"iaBztQzz8cJ5BsLZAC7A98RUh8GHEah0GP5ntaw1ZpK1PI4WqBLC5kNIfPxppul/3NOzpLvcHhpqbJymzDf6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0129261117bec211c17c609bbc7d919a3640cac289c7ee5d0b4a55e1ce7b64ce","last_reissued_at":"2026-05-17T23:59:22.827223Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:22.827223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning algorithms based on generalized Gibbs ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-th","physics.comp-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"Axel Cortes Cubero, Tatjana Puskarov","submitted_at":"2018-04-10T14:04:18Z","abstract_excerpt":"Machine learning algorithms often take inspiration from established results and knowledge from statistical physics. A prototypical example is the Boltzmann machine algorithm for supervised learning, which utilizes knowledge of classical thermal partition functions and the Boltzmann distribution. Recently, a quantum version of the Boltzmann machine was introduced by Amin, et. al., however, non-commutativity of quantum operators renders the training process by minimizing a cost function inefficient. Recent advances in the study of non-equilibrium quantum integrable systems, which never thermaliz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03546","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1804.03546","created_at":"2026-05-17T23:59:22.827295+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.03546v3","created_at":"2026-05-17T23:59:22.827295+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.03546","created_at":"2026-05-17T23:59:22.827295+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEUSMEIXX3BB","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEUSMEIXX3BBDQL4","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEUSMEIX","created_at":"2026-05-18T12:32:13.499390+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/AEUSMEIXX3BBDQL4MCN3Y7MRTI","json":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI.json","graph_json":"https://pith.science/api/pith-number/AEUSMEIXX3BBDQL4MCN3Y7MRTI/graph.json","events_json":"https://pith.science/api/pith-number/AEUSMEIXX3BBDQL4MCN3Y7MRTI/events.json","paper":"https://pith.science/paper/AEUSMEIX"},"agent_actions":{"view_html":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI","download_json":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI.json","view_paper":"https://pith.science/paper/AEUSMEIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.03546&json=true","fetch_graph":"https://pith.science/api/pith-number/AEUSMEIXX3BBDQL4MCN3Y7MRTI/graph.json","fetch_events":"https://pith.science/api/pith-number/AEUSMEIXX3BBDQL4MCN3Y7MRTI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI/action/storage_attestation","attest_author":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI/action/author_attestation","sign_citation":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI/action/citation_signature","submit_replication":"https://pith.science/pith/AEUSMEIXX3BBDQL4MCN3Y7MRTI/action/replication_record"}},"created_at":"2026-05-17T23:59:22.827295+00:00","updated_at":"2026-05-17T23:59:22.827295+00:00"}