{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:CZZF2A6G3V6JFAORIP7IQ3GCNM","short_pith_number":"pith:CZZF2A6G","schema_version":"1.0","canonical_sha256":"16725d03c6dd7c9281d143fe886cc26b379c98e59cabed1f8b4f7232d066ebbc","source":{"kind":"arxiv","id":"1404.0150","version":1},"attestation_state":"computed","paper":{"title":"Replica exchange molecular dynamics optimization of tensor network states for quantum many-body systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.str-el","authors_text":"Chao Wang, Guang-Can Guo, Lixin He, Wenyuan Liu, Yanbin Li, YongJian Han, Yuyang Lao","submitted_at":"2014-04-01T07:55:45Z","abstract_excerpt":"The tensor network states (TNS) methods combined with Monte Carlo (MC) techniques have been proved a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling techniques, by mapping the energy function of the TNS to that of a classical dynamical sys"},"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":"1404.0150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.str-el","submitted_at":"2014-04-01T07:55:45Z","cross_cats_sorted":[],"title_canon_sha256":"5370f589976f8bb35f88c46f1fd443f468f9cbdffbcf2c02e71cdce1b87db31e","abstract_canon_sha256":"46aea7b6ddf9cef9f4ba1156bbcd3fdcd975b4cd44200c3bfe3e08e5b8510719"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:43:43.503266Z","signature_b64":"xu+TLm2YSRxhCQpkZvscR51OheXVk6TXh21+REXznODV6Nrc7cEwSnGWoUoIc74FYaqbYCk/plvln77G0D//Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16725d03c6dd7c9281d143fe886cc26b379c98e59cabed1f8b4f7232d066ebbc","last_reissued_at":"2026-05-18T01:43:43.502719Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:43:43.502719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Replica exchange molecular dynamics optimization of tensor network states for quantum many-body systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.str-el","authors_text":"Chao Wang, Guang-Can Guo, Lixin He, Wenyuan Liu, Yanbin Li, YongJian Han, Yuyang Lao","submitted_at":"2014-04-01T07:55:45Z","abstract_excerpt":"The tensor network states (TNS) methods combined with Monte Carlo (MC) techniques have been proved a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling techniques, by mapping the energy function of the TNS to that of a classical dynamical sys"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.0150","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":"1404.0150","created_at":"2026-05-18T01:43:43.502812+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.0150v1","created_at":"2026-05-18T01:43:43.502812+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.0150","created_at":"2026-05-18T01:43:43.502812+00:00"},{"alias_kind":"pith_short_12","alias_value":"CZZF2A6G3V6J","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_16","alias_value":"CZZF2A6G3V6JFAOR","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_8","alias_value":"CZZF2A6G","created_at":"2026-05-18T12:28:25.294606+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/CZZF2A6G3V6JFAORIP7IQ3GCNM","json":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM.json","graph_json":"https://pith.science/api/pith-number/CZZF2A6G3V6JFAORIP7IQ3GCNM/graph.json","events_json":"https://pith.science/api/pith-number/CZZF2A6G3V6JFAORIP7IQ3GCNM/events.json","paper":"https://pith.science/paper/CZZF2A6G"},"agent_actions":{"view_html":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM","download_json":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM.json","view_paper":"https://pith.science/paper/CZZF2A6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.0150&json=true","fetch_graph":"https://pith.science/api/pith-number/CZZF2A6G3V6JFAORIP7IQ3GCNM/graph.json","fetch_events":"https://pith.science/api/pith-number/CZZF2A6G3V6JFAORIP7IQ3GCNM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM/action/storage_attestation","attest_author":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM/action/author_attestation","sign_citation":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM/action/citation_signature","submit_replication":"https://pith.science/pith/CZZF2A6G3V6JFAORIP7IQ3GCNM/action/replication_record"}},"created_at":"2026-05-18T01:43:43.502812+00:00","updated_at":"2026-05-18T01:43:43.502812+00:00"}