{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AR53EUDOC3EFSHMUUIUDVPWUHG","short_pith_number":"pith:AR53EUDO","schema_version":"1.0","canonical_sha256":"047bb2506e16c8591d94a2283abed439a938bdbef56547ed587c1cda369185b7","source":{"kind":"arxiv","id":"2305.15868","version":1},"attestation_state":"computed","paper":{"title":"Accurate determination of low-energy eigenspectra with multi-target matrix product states","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.str-el","authors_text":"Guanglei Xu, Haijun Liao, Runze Chi, Tao Xiang, Tong Liu, Xuan Li, Yibin Guo, Zongsheng Zhou","submitted_at":"2023-05-25T08:59:49Z","abstract_excerpt":"Determining the low-energy eigenspectra of quantum many-body systems is a long-standing challenge in physics. In this work, we solve this problem by introducing two novel algorithms to determine low-energy eigenstates based on a compact matrix product state (MPS) representation of the multiple targeted eigenstates. The first algorithm utilizes a canonicalization approach that takes advantage of the imaginary-time evolution of multi-target MPS, offering faster convergence and ease of implementation. The second algorithm employs a variational approach that optimizes local tensors on the Grassman"},"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":"2305.15868","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.str-el","submitted_at":"2023-05-25T08:59:49Z","cross_cats_sorted":[],"title_canon_sha256":"e63f8835b9a33fd38cd5d53643e90ea25d75d297a247eee3009cca86c1ae0ca6","abstract_canon_sha256":"8c8e095c41dffcca158cf615bae5fe103271fe8db03ef76cec4ca9759eaa963c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:56.998559Z","signature_b64":"aNuWyQKgEvxwE2Kn/frmpfglPF0DPiOPLO8w6n1Tw5Wzup4vE4TBW3VpQbNMmVQkxtKVsqH9XHyEez9unrWSDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"047bb2506e16c8591d94a2283abed439a938bdbef56547ed587c1cda369185b7","last_reissued_at":"2026-07-05T06:13:56.998125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:56.998125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accurate determination of low-energy eigenspectra with multi-target matrix product states","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.str-el","authors_text":"Guanglei Xu, Haijun Liao, Runze Chi, Tao Xiang, Tong Liu, Xuan Li, Yibin Guo, Zongsheng Zhou","submitted_at":"2023-05-25T08:59:49Z","abstract_excerpt":"Determining the low-energy eigenspectra of quantum many-body systems is a long-standing challenge in physics. In this work, we solve this problem by introducing two novel algorithms to determine low-energy eigenstates based on a compact matrix product state (MPS) representation of the multiple targeted eigenstates. The first algorithm utilizes a canonicalization approach that takes advantage of the imaginary-time evolution of multi-target MPS, offering faster convergence and ease of implementation. The second algorithm employs a variational approach that optimizes local tensors on the Grassman"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15868","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/2305.15868/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":"2305.15868","created_at":"2026-07-05T06:13:56.998176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15868v1","created_at":"2026-07-05T06:13:56.998176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15868","created_at":"2026-07-05T06:13:56.998176+00:00"},{"alias_kind":"pith_short_12","alias_value":"AR53EUDOC3EF","created_at":"2026-07-05T06:13:56.998176+00:00"},{"alias_kind":"pith_short_16","alias_value":"AR53EUDOC3EFSHMU","created_at":"2026-07-05T06:13:56.998176+00:00"},{"alias_kind":"pith_short_8","alias_value":"AR53EUDO","created_at":"2026-07-05T06:13:56.998176+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.24109","citing_title":"Multi-Target Density Matrix Renormalization Group X algorithm and its application to circuit quantum electrodynamics","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG","json":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG.json","graph_json":"https://pith.science/api/pith-number/AR53EUDOC3EFSHMUUIUDVPWUHG/graph.json","events_json":"https://pith.science/api/pith-number/AR53EUDOC3EFSHMUUIUDVPWUHG/events.json","paper":"https://pith.science/paper/AR53EUDO"},"agent_actions":{"view_html":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG","download_json":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG.json","view_paper":"https://pith.science/paper/AR53EUDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15868&json=true","fetch_graph":"https://pith.science/api/pith-number/AR53EUDOC3EFSHMUUIUDVPWUHG/graph.json","fetch_events":"https://pith.science/api/pith-number/AR53EUDOC3EFSHMUUIUDVPWUHG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG/action/storage_attestation","attest_author":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG/action/author_attestation","sign_citation":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG/action/citation_signature","submit_replication":"https://pith.science/pith/AR53EUDOC3EFSHMUUIUDVPWUHG/action/replication_record"}},"created_at":"2026-07-05T06:13:56.998176+00:00","updated_at":"2026-07-05T06:13:56.998176+00:00"}