{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PHFA4VBTQNPQAW6SEZQL36U7N5","short_pith_number":"pith:PHFA4VBT","schema_version":"1.0","canonical_sha256":"79ca0e5433835f005bd22660bdfa9f6f61ef53f6072aa053ccb96c1856ea2702","source":{"kind":"arxiv","id":"2206.04701","version":1},"attestation_state":"computed","paper":{"title":"Efficient tensor network simulation of quantum many-body physics on sparse graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","cond-mat.str-el"],"primary_cat":"quant-ph","authors_text":"Brian Swingle, Subhayan Sahu","submitted_at":"2022-06-09T18:00:03Z","abstract_excerpt":"We study tensor network states defined on an underlying graph which is sparsely connected. Generic sparse graphs are expander graphs with a high probability, and one can represent volume law entangled states efficiently with only polynomial resources. We find that message-passing inference algorithms such as belief propagation can lead to efficient computation of local expectation values for a class of tensor network states defined on sparse graphs. As applications, we study local properties of square root states, graph states, and also employ this method to variationally prepare ground states"},"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":"2206.04701","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-09T18:00:03Z","cross_cats_sorted":["cond-mat.stat-mech","cond-mat.str-el"],"title_canon_sha256":"6ab59118dbc92f90cc657271e94751363938d01c3c82968894b5326b0112115a","abstract_canon_sha256":"bc0ec886bd3fe0065ffcccb58ffa376f346c4f447ffe325e14f4e297c81d157c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:30:43.351581Z","signature_b64":"+0yzOVpz30Hh0jJonv9/LWx6kOtfZTuiQ2Cck60c9/chYS0NCJd4K1cmCs58J3tJt6kUHu6z53URMojwkAUoAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79ca0e5433835f005bd22660bdfa9f6f61ef53f6072aa053ccb96c1856ea2702","last_reissued_at":"2026-07-05T04:30:43.351150Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:30:43.351150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient tensor network simulation of quantum many-body physics on sparse graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","cond-mat.str-el"],"primary_cat":"quant-ph","authors_text":"Brian Swingle, Subhayan Sahu","submitted_at":"2022-06-09T18:00:03Z","abstract_excerpt":"We study tensor network states defined on an underlying graph which is sparsely connected. Generic sparse graphs are expander graphs with a high probability, and one can represent volume law entangled states efficiently with only polynomial resources. We find that message-passing inference algorithms such as belief propagation can lead to efficient computation of local expectation values for a class of tensor network states defined on sparse graphs. As applications, we study local properties of square root states, graph states, and also employ this method to variationally prepare ground states"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.04701","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/2206.04701/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":"2206.04701","created_at":"2026-07-05T04:30:43.351204+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.04701v1","created_at":"2026-07-05T04:30:43.351204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.04701","created_at":"2026-07-05T04:30:43.351204+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHFA4VBTQNPQ","created_at":"2026-07-05T04:30:43.351204+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHFA4VBTQNPQAW6S","created_at":"2026-07-05T04:30:43.351204+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHFA4VBT","created_at":"2026-07-05T04:30:43.351204+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.08407","citing_title":"Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03228","citing_title":"Belief Propagation and Tensor Network Expansions for Many-Body Quantum Systems: Rigorous Results and Fundamental Limits","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21919","citing_title":"Algorithmic Locality via Provable Convergence in Quantum Tensor Networks","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15427","citing_title":"Tensor Networks with Belief Propagation Cannot Feasibly Simulate Google's Quantum Echoes Experiment","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20180","citing_title":"Tensor network surrogate models for variational quantum computation","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5","json":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5.json","graph_json":"https://pith.science/api/pith-number/PHFA4VBTQNPQAW6SEZQL36U7N5/graph.json","events_json":"https://pith.science/api/pith-number/PHFA4VBTQNPQAW6SEZQL36U7N5/events.json","paper":"https://pith.science/paper/PHFA4VBT"},"agent_actions":{"view_html":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5","download_json":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5.json","view_paper":"https://pith.science/paper/PHFA4VBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.04701&json=true","fetch_graph":"https://pith.science/api/pith-number/PHFA4VBTQNPQAW6SEZQL36U7N5/graph.json","fetch_events":"https://pith.science/api/pith-number/PHFA4VBTQNPQAW6SEZQL36U7N5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5/action/storage_attestation","attest_author":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5/action/author_attestation","sign_citation":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5/action/citation_signature","submit_replication":"https://pith.science/pith/PHFA4VBTQNPQAW6SEZQL36U7N5/action/replication_record"}},"created_at":"2026-07-05T04:30:43.351204+00:00","updated_at":"2026-07-05T04:30:43.351204+00:00"}