{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:L3GZKMHCATFDBT7KZ2IYDNMRJW","short_pith_number":"pith:L3GZKMHC","schema_version":"1.0","canonical_sha256":"5ecd9530e204ca30cfeace9181b5914d96367c2be2bfdc0c26f3c53b105d8c6c","source":{"kind":"arxiv","id":"2002.01935","version":4},"attestation_state":"computed","paper":{"title":"Hyper-optimized tensor network contraction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Johnnie Gray, Stefanos Kourtis","submitted_at":"2020-02-05T19:00:00Z","abstract_excerpt":"Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a vari"},"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":"2002.01935","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"quant-ph","submitted_at":"2020-02-05T19:00:00Z","cross_cats_sorted":["cond-mat.dis-nn","physics.comp-ph"],"title_canon_sha256":"a3af23066b98538c7177b7ada0145d003b84e8f72407e154aa9d6a31dd177617","abstract_canon_sha256":"b851ea4b546e3818a36839f1dd1ecc5103220a41e5a043f9913fe5ca978c7f52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:23:42.340405Z","signature_b64":"R0ZISR7eO+BaakzmUAtnKhhb/vb2o3kcN2wiSWFjjCnTnMXVyYqn1GhHyhc8Am0MalAZTb2z15UpaMiwD7vlDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ecd9530e204ca30cfeace9181b5914d96367c2be2bfdc0c26f3c53b105d8c6c","last_reissued_at":"2026-07-05T02:23:42.339897Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:23:42.339897Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyper-optimized tensor network contraction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Johnnie Gray, Stefanos Kourtis","submitted_at":"2020-02-05T19:00:00Z","abstract_excerpt":"Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a vari"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.01935","kind":"arxiv","version":4},"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/2002.01935/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":"2002.01935","created_at":"2026-07-05T02:23:42.339955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.01935v4","created_at":"2026-07-05T02:23:42.339955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.01935","created_at":"2026-07-05T02:23:42.339955+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3GZKMHCATFD","created_at":"2026-07-05T02:23:42.339955+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3GZKMHCATFDBT7K","created_at":"2026-07-05T02:23:42.339955+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3GZKMHC","created_at":"2026-07-05T02:23:42.339955+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08707","citing_title":"Simulating quantum circuits with a neural statebank","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW","json":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW.json","graph_json":"https://pith.science/api/pith-number/L3GZKMHCATFDBT7KZ2IYDNMRJW/graph.json","events_json":"https://pith.science/api/pith-number/L3GZKMHCATFDBT7KZ2IYDNMRJW/events.json","paper":"https://pith.science/paper/L3GZKMHC"},"agent_actions":{"view_html":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW","download_json":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW.json","view_paper":"https://pith.science/paper/L3GZKMHC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.01935&json=true","fetch_graph":"https://pith.science/api/pith-number/L3GZKMHCATFDBT7KZ2IYDNMRJW/graph.json","fetch_events":"https://pith.science/api/pith-number/L3GZKMHCATFDBT7KZ2IYDNMRJW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW/action/storage_attestation","attest_author":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW/action/author_attestation","sign_citation":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW/action/citation_signature","submit_replication":"https://pith.science/pith/L3GZKMHCATFDBT7KZ2IYDNMRJW/action/replication_record"}},"created_at":"2026-07-05T02:23:42.339955+00:00","updated_at":"2026-07-05T02:23:42.339955+00:00"}