{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","short_pith_number":"pith:WBPXEOEV","canonical_record":{"source":{"id":"2607.28570","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-30T17:34:48Z","cross_cats_sorted":[],"title_canon_sha256":"f0bbd7880f133505fd45a65cb4a2a933091e8264eff2bfd73b2181f59ec0c40d","abstract_canon_sha256":"6b8204ff588af40c9186d2a517ebc574b60f4a27863f56cff27e05718d646981"},"schema_version":"1.0"},"canonical_sha256":"b05f7238955347e54a9380ca96b67f431ecee5c6bbd3f96630625bdcb5860d87","source":{"kind":"arxiv","id":"2607.28570","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28570","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28570v1","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28570","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"WBPXEOEVKND6","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_16","alias_value":"WBPXEOEVKND6KSUT","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_8","alias_value":"WBPXEOEV","created_at":"2026-07-31T01:38:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","target":"record","payload":{"canonical_record":{"source":{"id":"2607.28570","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-30T17:34:48Z","cross_cats_sorted":[],"title_canon_sha256":"f0bbd7880f133505fd45a65cb4a2a933091e8264eff2bfd73b2181f59ec0c40d","abstract_canon_sha256":"6b8204ff588af40c9186d2a517ebc574b60f4a27863f56cff27e05718d646981"},"schema_version":"1.0"},"canonical_sha256":"b05f7238955347e54a9380ca96b67f431ecee5c6bbd3f96630625bdcb5860d87","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b05f7238955347e54a9380ca96b67f431ecee5c6bbd3f96630625bdcb5860d87","last_reissued_at":"2026-07-31T01:38:35.077734Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:38:35.077734Z"},"source_kind":"arxiv","source_id":"2607.28570","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T01:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l2tbMMKUXjHiBp+QCpSZbBc4kNOLnf90fLoWIUAGJVIwRJD16UJZ9E/OaCKlIl7chhUMm35Xm3FWXilJuSBFDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:37.122077Z"},"content_sha256":"f4c98f5813a664b92083a20d5068bf4b839525c4de617ab7c0c9241619a7e9a9","schema_version":"1.0","event_id":"sha256:f4c98f5813a664b92083a20d5068bf4b839525c4de617ab7c0c9241619a7e9a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Benchmarking Quantum Simulations of the Lipkin-Meshkov-Glick Model Using Large Tensor Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Brian J. McDermott, Henry Zou, Jerimiah Wright, Joan \\'Etude Arrow, Maggie Bao, Rushil Dandamudi, Vardaan Sahgal","submitted_at":"2026-07-30T17:34:48Z","abstract_excerpt":"As quantum computing matures, it is critical to benchmark its real-world problem solving performance against competitive classical methods, such as tensor networks. In this work, we leverage the Density Matrix Renormalization Group (DMRG) algorithm to compute ground state energies of the Lipkin Meshkov Glick (LMG) model as a comparative benchmark against popular noisy intermediate-scale (NISQ) algorithms like the Variational Quantum Eigensolver (VQE) and Sample-Based Quantum Diagonalization (SQD) method. By running DMRG on the NERSC Perlmutter supercomputer, we provide one of the largest LMG g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28570","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/2607.28570/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T01:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XkQ0PfPGMRp1nt0inbqJ3BWI+wNl3wDwGRVvv9rkKcf/SbUxGG0/hT7ZP9Vm0pLpgeRm5warjvTOgh0sdvXtAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:37.122570Z"},"content_sha256":"1f40792bd5fbadcc25962c05b575dc02a311b7eee21df3d02a65349293389b94","schema_version":"1.0","event_id":"sha256:1f40792bd5fbadcc25962c05b575dc02a311b7eee21df3d02a65349293389b94"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.22331/q-2024-12-11-1559 resolves to 'HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware'. A reader following the printed text alone cannot reach it.","snippet":"N. P. Sawaya et al., “HamLib: A library of hamiltonians for benchmarking quan- tum algorithms and hardware,”Quantum, vol. 8, p. 1559, 2024.doi: 10.22331/q-2024- 12-11-1559","arxiv_id":"2607.28570","detector":"doi_compliance","evidence":{"ref_index":6,"verdict_class":"incontrovertible","resolved_title":"HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware","printed_excerpt":"10.22331/q-2024-","reconstructed_doi":"10.22331/q-2024-12-11-1559"},"severity":"advisory","ref_index":6,"audited_at":"2026-08-03T03:58:25.757313Z","event_type":"pith.integrity.v1","detected_doi":"10.22331/q-2024-12-11-1559","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"8c552eabb753b1c862c92df720738f7118aabf7bf2649cff41b814bce1bf88ec","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":17543,"payload_sha256":"3451f1b4bd25247177aa83c0e4650db15972f47ed737e3d3fa1ba4518eef09f3","signature_b64":"zTgIiY/2pRAYYzM48vq11NmneG3oSJKhofqEGrhRgZnnKzIvxNa4aNwKMA3Fqua0AypX3tPpt6UhLB5SLFReDg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-03T03:58:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PoqERTOxh46spBDJeznUY0KgoV1jGU/G55UmMAFc2BgXJQsf8qw95ic34oTU3ZMk3iQKyzFCofpJ/fT0W0ARAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:37.146966Z"},"content_sha256":"3a1453fb65702b8d654ea61ef99fcfd8b98b3491e319484dfa467deea0f26a12","schema_version":"1.0","event_id":"sha256:3a1453fb65702b8d654ea61ef99fcfd8b98b3491e319484dfa467deea0f26a12"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.22331/q-2024-12-11-1559) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"N. P. Sawaya et al., “HamLib: A library of hamiltonians for benchmarking quan- tum algorithms and hardware,”Quantum, vol. 8, p. 1559, 2024.doi: 10.22331/q-2024- 12-11-1559","arxiv_id":"2607.28570","detector":"doi_compliance","evidence":{"ref_index":6,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.22331/q-2024-","reconstructed_doi":"10.22331/q-2024-12-11-1559"},"severity":"advisory","ref_index":6,"audited_at":"2026-07-31T03:31:41.548079Z","event_type":"pith.integrity.v1","detected_doi":"10.22331/q-2024-12-11-1559","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"9d6e318865ef50ad100e343016a3c0d270556692fe71b9e6b34de27361bb0b3e","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14195,"payload_sha256":"760c1f8588aeda2de878f7a3efe433ba599724931927441665a841300de509d3","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T03:36:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VFh/BWZPdkrmLl6g4o5DClusI7FzY55l9zkVsnb+R0rTXqpGdTVCqdRETwVysJZvSDRZCfeWY06VQTbjeAmcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:37.147422Z"},"content_sha256":"ae171c196331f94cf0c65948e101c178a158b4f0801db1f19667bf3d3b560616","schema_version":"1.0","event_id":"sha256:ae171c196331f94cf0c65948e101c178a158b4f0801db1f19667bf3d3b560616"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/bundle.json","state_url":"https://pith.science/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T02:59:37Z","links":{"resolver":"https://pith.science/pith/WBPXEOEVKND6KSUTQDFJNNT7IM","bundle":"https://pith.science/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/bundle.json","state":"https://pith.science/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WBPXEOEVKND6KSUTQDFJNNT7IM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WBPXEOEVKND6KSUTQDFJNNT7IM","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6b8204ff588af40c9186d2a517ebc574b60f4a27863f56cff27e05718d646981","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-30T17:34:48Z","title_canon_sha256":"f0bbd7880f133505fd45a65cb4a2a933091e8264eff2bfd73b2181f59ec0c40d"},"schema_version":"1.0","source":{"id":"2607.28570","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28570","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28570v1","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28570","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"WBPXEOEVKND6","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_16","alias_value":"WBPXEOEVKND6KSUT","created_at":"2026-07-31T01:38:35Z"},{"alias_kind":"pith_short_8","alias_value":"WBPXEOEV","created_at":"2026-07-31T01:38:35Z"}],"graph_snapshots":[{"event_id":"sha256:1f40792bd5fbadcc25962c05b575dc02a311b7eee21df3d02a65349293389b94","target":"graph","created_at":"2026-07-31T01:38:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.28570/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As quantum computing matures, it is critical to benchmark its real-world problem solving performance against competitive classical methods, such as tensor networks. In this work, we leverage the Density Matrix Renormalization Group (DMRG) algorithm to compute ground state energies of the Lipkin Meshkov Glick (LMG) model as a comparative benchmark against popular noisy intermediate-scale (NISQ) algorithms like the Variational Quantum Eigensolver (VQE) and Sample-Based Quantum Diagonalization (SQD) method. By running DMRG on the NERSC Perlmutter supercomputer, we provide one of the largest LMG g","authors_text":"Brian J. McDermott, Henry Zou, Jerimiah Wright, Joan \\'Etude Arrow, Maggie Bao, Rushil Dandamudi, Vardaan Sahgal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-30T17:34:48Z","title":"Benchmarking Quantum Simulations of the Lipkin-Meshkov-Glick Model Using Large Tensor Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28570","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f4c98f5813a664b92083a20d5068bf4b839525c4de617ab7c0c9241619a7e9a9","target":"record","created_at":"2026-07-31T01:38:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6b8204ff588af40c9186d2a517ebc574b60f4a27863f56cff27e05718d646981","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-30T17:34:48Z","title_canon_sha256":"f0bbd7880f133505fd45a65cb4a2a933091e8264eff2bfd73b2181f59ec0c40d"},"schema_version":"1.0","source":{"id":"2607.28570","kind":"arxiv","version":1}},"canonical_sha256":"b05f7238955347e54a9380ca96b67f431ecee5c6bbd3f96630625bdcb5860d87","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b05f7238955347e54a9380ca96b67f431ecee5c6bbd3f96630625bdcb5860d87","first_computed_at":"2026-07-31T01:38:35.077734Z","kind":"pith_receipt","last_reissued_at":"2026-07-31T01:38:35.077734Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.28570","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:3a1453fb65702b8d654ea61ef99fcfd8b98b3491e319484dfa467deea0f26a12","sha256:ae171c196331f94cf0c65948e101c178a158b4f0801db1f19667bf3d3b560616"]}],"invalid_events":[],"applied_event_ids":["sha256:f4c98f5813a664b92083a20d5068bf4b839525c4de617ab7c0c9241619a7e9a9","sha256:1f40792bd5fbadcc25962c05b575dc02a311b7eee21df3d02a65349293389b94"],"state_sha256":"75e542fb090dbe223e15940718a4bb1963d3499a769ddd79fac97d52925fb498"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cbobB7APq77Jlh0NGSb7IbseFJJPvWO7XEpb4yhouMETExkCOUnNQvroPdeA+EXRmbWTH3dY3mKdOrU2QcvpBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T02:59:37.149888Z","bundle_sha256":"81294df791990aad2a448b55c6fe2b3bdb374eaa731168aabd133ea453872bb6"}}