{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JLPKFKW4XI4KJEDBJ6HX3A663B","short_pith_number":"pith:JLPKFKW4","schema_version":"1.0","canonical_sha256":"4adea2aadcba38a490614f8f7d83ded8581cfd82b4d4ee8a28b78b4d95cc2fe1","source":{"kind":"arxiv","id":"2506.12128","version":1},"attestation_state":"computed","paper":{"title":"Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-lat","hep-ph"],"primary_cat":"quant-ph","authors_text":"Michael Spannowsky, Timur Sypchenko, Vishal S. Ngairangbam","submitted_at":"2025-06-13T18:00:01Z","abstract_excerpt":"We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum many-body wavefunctions. Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space. This overcomes limitations of Markov Chain Monte Carlo (MCMC) and autoregressive methods, especially in regimes with long-range correlations and volume-law entanglement. Applied to the transverse-field Ising model"},"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":"2506.12128","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-06-13T18:00:01Z","cross_cats_sorted":["cs.LG","hep-lat","hep-ph"],"title_canon_sha256":"33b1bd448d56f31770c3f0028df58f73da87adc002c243e7fd53752e75f0285a","abstract_canon_sha256":"64320d64d54d7751750f9e7881713e487ed026fc4d1d18aab1ecfd5fbba50fbe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:21.615547Z","signature_b64":"KiK5OELyJoZGyAlFPeX7Kli5WWZYnGL3tYhlYP3nG4Uc1UuzNa6Fgw27Rc3cUDboGonoBmUKDg2ThTfPnLwtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4adea2aadcba38a490614f8f7d83ded8581cfd82b4d4ee8a28b78b4d95cc2fe1","last_reissued_at":"2026-07-05T11:21:21.615073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:21.615073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-lat","hep-ph"],"primary_cat":"quant-ph","authors_text":"Michael Spannowsky, Timur Sypchenko, Vishal S. Ngairangbam","submitted_at":"2025-06-13T18:00:01Z","abstract_excerpt":"We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum many-body wavefunctions. Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space. This overcomes limitations of Markov Chain Monte Carlo (MCMC) and autoregressive methods, especially in regimes with long-range correlations and volume-law entanglement. Applied to the transverse-field Ising model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12128","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/2506.12128/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":"2506.12128","created_at":"2026-07-05T11:21:21.615130+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12128v1","created_at":"2026-07-05T11:21:21.615130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12128","created_at":"2026-07-05T11:21:21.615130+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLPKFKW4XI4K","created_at":"2026-07-05T11:21:21.615130+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLPKFKW4XI4KJEDB","created_at":"2026-07-05T11:21:21.615130+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLPKFKW4","created_at":"2026-07-05T11:21:21.615130+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/JLPKFKW4XI4KJEDBJ6HX3A663B","json":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B.json","graph_json":"https://pith.science/api/pith-number/JLPKFKW4XI4KJEDBJ6HX3A663B/graph.json","events_json":"https://pith.science/api/pith-number/JLPKFKW4XI4KJEDBJ6HX3A663B/events.json","paper":"https://pith.science/paper/JLPKFKW4"},"agent_actions":{"view_html":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B","download_json":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B.json","view_paper":"https://pith.science/paper/JLPKFKW4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12128&json=true","fetch_graph":"https://pith.science/api/pith-number/JLPKFKW4XI4KJEDBJ6HX3A663B/graph.json","fetch_events":"https://pith.science/api/pith-number/JLPKFKW4XI4KJEDBJ6HX3A663B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B/action/storage_attestation","attest_author":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B/action/author_attestation","sign_citation":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B/action/citation_signature","submit_replication":"https://pith.science/pith/JLPKFKW4XI4KJEDBJ6HX3A663B/action/replication_record"}},"created_at":"2026-07-05T11:21:21.615130+00:00","updated_at":"2026-07-05T11:21:21.615130+00:00"}