{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ZO2PU47PI4FRKBAXUGFJGTFLY2","short_pith_number":"pith:ZO2PU47P","canonical_record":{"source":{"id":"2106.12974","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-06-24T12:51:00Z","cross_cats_sorted":["cs.LG","quant-ph","stat.ML"],"title_canon_sha256":"c3b26832fc4e30ce36701a8e8a19ba199808e8d7335c49cbc25312b23fdf1f5d","abstract_canon_sha256":"b4c83d373e5a7cb2ff8ff80b599b354a98556b6e978ff03ac2cb32a0044750a0"},"schema_version":"1.0"},"canonical_sha256":"cbb4fa73ef470b150417a18a934cabc6b4046472e2496a310257c8b59301b2a8","source":{"kind":"arxiv","id":"2106.12974","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12974","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12974v2","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12974","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZO2PU47PI4FR","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_16","alias_value":"ZO2PU47PI4FRKBAX","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_8","alias_value":"ZO2PU47P","created_at":"2026-07-05T05:37:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ZO2PU47PI4FRKBAXUGFJGTFLY2","target":"record","payload":{"canonical_record":{"source":{"id":"2106.12974","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-06-24T12:51:00Z","cross_cats_sorted":["cs.LG","quant-ph","stat.ML"],"title_canon_sha256":"c3b26832fc4e30ce36701a8e8a19ba199808e8d7335c49cbc25312b23fdf1f5d","abstract_canon_sha256":"b4c83d373e5a7cb2ff8ff80b599b354a98556b6e978ff03ac2cb32a0044750a0"},"schema_version":"1.0"},"canonical_sha256":"cbb4fa73ef470b150417a18a934cabc6b4046472e2496a310257c8b59301b2a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:51.924539Z","signature_b64":"QHG3R7Hs/HaSq2zeuHkFjZVSVRKmTXMjtckxLUmLfWlS37yG3iyVDOXzYmusx5s/a1ONt48Abv9+fll0SY4vBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbb4fa73ef470b150417a18a934cabc6b4046472e2496a310257c8b59301b2a8","last_reissued_at":"2026-07-05T05:37:51.923988Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:51.923988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.12974","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:37:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CCbW7rcQNjrL2Y28aFToxrZop83Oqxt5PnIIrU+yJryL9agz1mwyjRSTlVlFfPReAWMQxZg96lz5RJle8lopDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:50:28.266375Z"},"content_sha256":"1ae28554543a688517f811e0a0affe5a652dc2eef7988d5e5649ac0b149051d0","schema_version":"1.0","event_id":"sha256:1ae28554543a688517f811e0a0affe5a652dc2eef7988d5e5649ac0b149051d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ZO2PU47PI4FRKBAXUGFJGTFLY2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tensor networks for unsupervised machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","quant-ph","stat.ML"],"primary_cat":"cond-mat.stat-mech","authors_text":"Jiang Zhang, Jing Liu, Pan Zhang, Sujie Li","submitted_at":"2021-06-24T12:51:00Z","abstract_excerpt":"Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting classical computation and quantum computation. Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12974","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/2106.12974/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:37:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jtwb4eIqBpY+pkcUxzS6+L1pM1PqGUtAGxlkL1dTr/OTquhwun+S/ZqCRKBcYy8I+k/RWGcBeXsihPttICy9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:50:28.267572Z"},"content_sha256":"150327e8bf3f3e2350d5b7dfe348afdc1d2d1de060301fdb6be2c7eb5fb8d181","schema_version":"1.0","event_id":"sha256:150327e8bf3f3e2350d5b7dfe348afdc1d2d1de060301fdb6be2c7eb5fb8d181"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/bundle.json","state_url":"https://pith.science/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T10:50:28Z","links":{"resolver":"https://pith.science/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2","bundle":"https://pith.science/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/bundle.json","state":"https://pith.science/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZO2PU47PI4FRKBAXUGFJGTFLY2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ZO2PU47PI4FRKBAXUGFJGTFLY2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b4c83d373e5a7cb2ff8ff80b599b354a98556b6e978ff03ac2cb32a0044750a0","cross_cats_sorted":["cs.LG","quant-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-06-24T12:51:00Z","title_canon_sha256":"c3b26832fc4e30ce36701a8e8a19ba199808e8d7335c49cbc25312b23fdf1f5d"},"schema_version":"1.0","source":{"id":"2106.12974","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12974","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12974v2","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12974","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZO2PU47PI4FR","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_16","alias_value":"ZO2PU47PI4FRKBAX","created_at":"2026-07-05T05:37:51Z"},{"alias_kind":"pith_short_8","alias_value":"ZO2PU47P","created_at":"2026-07-05T05:37:51Z"}],"graph_snapshots":[{"event_id":"sha256:150327e8bf3f3e2350d5b7dfe348afdc1d2d1de060301fdb6be2c7eb5fb8d181","target":"graph","created_at":"2026-07-05T05:37:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2106.12974/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting classical computation and quantum computation. Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models s","authors_text":"Jiang Zhang, Jing Liu, Pan Zhang, Sujie Li","cross_cats":["cs.LG","quant-ph","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-06-24T12:51:00Z","title":"Tensor networks for unsupervised machine learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12974","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1ae28554543a688517f811e0a0affe5a652dc2eef7988d5e5649ac0b149051d0","target":"record","created_at":"2026-07-05T05:37:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b4c83d373e5a7cb2ff8ff80b599b354a98556b6e978ff03ac2cb32a0044750a0","cross_cats_sorted":["cs.LG","quant-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-06-24T12:51:00Z","title_canon_sha256":"c3b26832fc4e30ce36701a8e8a19ba199808e8d7335c49cbc25312b23fdf1f5d"},"schema_version":"1.0","source":{"id":"2106.12974","kind":"arxiv","version":2}},"canonical_sha256":"cbb4fa73ef470b150417a18a934cabc6b4046472e2496a310257c8b59301b2a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cbb4fa73ef470b150417a18a934cabc6b4046472e2496a310257c8b59301b2a8","first_computed_at":"2026-07-05T05:37:51.923988Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:51.923988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QHG3R7Hs/HaSq2zeuHkFjZVSVRKmTXMjtckxLUmLfWlS37yG3iyVDOXzYmusx5s/a1ONt48Abv9+fll0SY4vBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:51.924539Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.12974","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ae28554543a688517f811e0a0affe5a652dc2eef7988d5e5649ac0b149051d0","sha256:150327e8bf3f3e2350d5b7dfe348afdc1d2d1de060301fdb6be2c7eb5fb8d181"],"state_sha256":"0a72e97a0c15b2a73b46722d1d9fad3b9b5ce3322bbf09a17640824642257841"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N6YHzJ1BMqOY4i+0EzVhDrxlrrPn+Zm1WFbN2dOS8RvNF4nBDwPOx8Um2asyCidvQgvF0pKiaehoHwyiV+aPBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T10:50:28.275748Z","bundle_sha256":"aa05c08fa9d25b599c9a5b09f6e4f715b28adce5815bbb46d8768836798c91d2"}}