{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:NZDAJGOK4ROA6IBKAZOEVFX4C5","short_pith_number":"pith:NZDAJGOK","schema_version":"1.0","canonical_sha256":"6e460499cae45c0f202a065c4a96fc1773b36655d0d2973749c181af7a9efcd7","source":{"kind":"arxiv","id":"1406.2666","version":1},"attestation_state":"computed","paper":{"title":"Unconstrained Tree Tensor Network: An adaptive gauge picture for enhanced performance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"Matteo Rizzi, Matthias Gerster, Pietro Silvi, Rosario Fazio, Simone Montangero, Tommaso Calarco","submitted_at":"2014-06-10T19:04:47Z","abstract_excerpt":"We introduce a variational algorithm to simulate quantum many-body states based on a tree tensor network ansatz which releases the isometry constraint usually imposed by the real-space renormalization coarse-graining: This additional numerical freedom, combined with the loop-free topology of the tree network, allows one to maximally exploit the internal gauge invariance of tensor networks, ultimately leading to a computationally flexible and efficient algorithm able to treat open and periodic boundary conditions on the same footing. We benchmark the novel approach against the 1D Ising model in"},"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":"1406.2666","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2014-06-10T19:04:47Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"440316be1692130ba25958518a116daaadae69fb81420f3306413be3b7570ee8","abstract_canon_sha256":"eb7927b8456e73a9f709d0e40133e731bcf0f4884b64037bad188e85a1a945ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:55.856856Z","signature_b64":"wj7H3QlLquL5uKztU9EeF6IkxXxx+pCnn7JXoHFLpO7WHQdtvZlG3VBmXGExPKgUFTToQXzt10UJx5YxKRNcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e460499cae45c0f202a065c4a96fc1773b36655d0d2973749c181af7a9efcd7","last_reissued_at":"2026-05-18T02:40:55.856283Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:55.856283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unconstrained Tree Tensor Network: An adaptive gauge picture for enhanced performance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"Matteo Rizzi, Matthias Gerster, Pietro Silvi, Rosario Fazio, Simone Montangero, Tommaso Calarco","submitted_at":"2014-06-10T19:04:47Z","abstract_excerpt":"We introduce a variational algorithm to simulate quantum many-body states based on a tree tensor network ansatz which releases the isometry constraint usually imposed by the real-space renormalization coarse-graining: This additional numerical freedom, combined with the loop-free topology of the tree network, allows one to maximally exploit the internal gauge invariance of tensor networks, ultimately leading to a computationally flexible and efficient algorithm able to treat open and periodic boundary conditions on the same footing. We benchmark the novel approach against the 1D Ising model in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.2666","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":""},"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":"1406.2666","created_at":"2026-05-18T02:40:55.856405+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.2666v1","created_at":"2026-05-18T02:40:55.856405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.2666","created_at":"2026-05-18T02:40:55.856405+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZDAJGOK4ROA","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZDAJGOK4ROA6IBK","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZDAJGOK","created_at":"2026-05-18T12:28:41.024544+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/NZDAJGOK4ROA6IBKAZOEVFX4C5","json":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5.json","graph_json":"https://pith.science/api/pith-number/NZDAJGOK4ROA6IBKAZOEVFX4C5/graph.json","events_json":"https://pith.science/api/pith-number/NZDAJGOK4ROA6IBKAZOEVFX4C5/events.json","paper":"https://pith.science/paper/NZDAJGOK"},"agent_actions":{"view_html":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5","download_json":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5.json","view_paper":"https://pith.science/paper/NZDAJGOK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.2666&json=true","fetch_graph":"https://pith.science/api/pith-number/NZDAJGOK4ROA6IBKAZOEVFX4C5/graph.json","fetch_events":"https://pith.science/api/pith-number/NZDAJGOK4ROA6IBKAZOEVFX4C5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5/action/storage_attestation","attest_author":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5/action/author_attestation","sign_citation":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5/action/citation_signature","submit_replication":"https://pith.science/pith/NZDAJGOK4ROA6IBKAZOEVFX4C5/action/replication_record"}},"created_at":"2026-05-18T02:40:55.856405+00:00","updated_at":"2026-05-18T02:40:55.856405+00:00"}