{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HGSGPB64R4ZINTNNOYTBXOHIB2","short_pith_number":"pith:HGSGPB64","schema_version":"1.0","canonical_sha256":"39a46787dc8f3286cdad76261bb8e80e814245351af8810ed296ed78d20dc970","source":{"kind":"arxiv","id":"1902.08539","version":1},"attestation_state":"computed","paper":{"title":"RTNI - A symbolic integrator for Haar-random tensor networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-th","math-ph","math.MP","math.PR"],"primary_cat":"quant-ph","authors_text":"Ion Nechita, Motohisa Fukuda, Robert Koenig","submitted_at":"2019-02-22T15:57:01Z","abstract_excerpt":"We provide a computer algebra package called Random Tensor Network Integrator (RTNI). It allows to compute averages of tensor networks containing multiple Haar-distributed random unitary matrices and deterministic symbolic tensors. Such tensor networks are represented as multigraphs, with vertices corresponding to tensors or random unitaries and edges corresponding to tensor contractions. Input and output spaces of random unitaries may be subdivided into arbitrary tensor factors, with dimensions treated symbolically. The algorithm implements the graphical Weingarten calculus and produces a wei"},"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":"1902.08539","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-02-22T15:57:01Z","cross_cats_sorted":["hep-th","math-ph","math.MP","math.PR"],"title_canon_sha256":"156bd32ff49ec2861bebbf4da2140d0731a42b86826a6c50f42cdb7cc3a03c47","abstract_canon_sha256":"d33e1d3c857d24d2b478f8bdd11ea95318eaa2a01bb3f2d5f99cac0a562bad94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:10:00.367801Z","signature_b64":"NYVTxoOrvBwbvzI8icQXG8QuYe403KU4PtuiqtKmXhlcQeFghohC0eklULBdVwNaIku5kCTvb+YaRFixG+HTBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39a46787dc8f3286cdad76261bb8e80e814245351af8810ed296ed78d20dc970","last_reissued_at":"2026-07-05T00:10:00.367375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:10:00.367375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RTNI - A symbolic integrator for Haar-random tensor networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-th","math-ph","math.MP","math.PR"],"primary_cat":"quant-ph","authors_text":"Ion Nechita, Motohisa Fukuda, Robert Koenig","submitted_at":"2019-02-22T15:57:01Z","abstract_excerpt":"We provide a computer algebra package called Random Tensor Network Integrator (RTNI). It allows to compute averages of tensor networks containing multiple Haar-distributed random unitary matrices and deterministic symbolic tensors. Such tensor networks are represented as multigraphs, with vertices corresponding to tensors or random unitaries and edges corresponding to tensor contractions. Input and output spaces of random unitaries may be subdivided into arbitrary tensor factors, with dimensions treated symbolically. The algorithm implements the graphical Weingarten calculus and produces a wei"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08539","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/1902.08539/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":"1902.08539","created_at":"2026-07-05T00:10:00.367432+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.08539v1","created_at":"2026-07-05T00:10:00.367432+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08539","created_at":"2026-07-05T00:10:00.367432+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGSGPB64R4ZI","created_at":"2026-07-05T00:10:00.367432+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGSGPB64R4ZINTNN","created_at":"2026-07-05T00:10:00.367432+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGSGPB64","created_at":"2026-07-05T00:10:00.367432+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/HGSGPB64R4ZINTNNOYTBXOHIB2","json":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2.json","graph_json":"https://pith.science/api/pith-number/HGSGPB64R4ZINTNNOYTBXOHIB2/graph.json","events_json":"https://pith.science/api/pith-number/HGSGPB64R4ZINTNNOYTBXOHIB2/events.json","paper":"https://pith.science/paper/HGSGPB64"},"agent_actions":{"view_html":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2","download_json":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2.json","view_paper":"https://pith.science/paper/HGSGPB64","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.08539&json=true","fetch_graph":"https://pith.science/api/pith-number/HGSGPB64R4ZINTNNOYTBXOHIB2/graph.json","fetch_events":"https://pith.science/api/pith-number/HGSGPB64R4ZINTNNOYTBXOHIB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2/action/storage_attestation","attest_author":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2/action/author_attestation","sign_citation":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2/action/citation_signature","submit_replication":"https://pith.science/pith/HGSGPB64R4ZINTNNOYTBXOHIB2/action/replication_record"}},"created_at":"2026-07-05T00:10:00.367432+00:00","updated_at":"2026-07-05T00:10:00.367432+00:00"}