{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7N5S4V7TZNLQZMPEP2U5T7CBLB","short_pith_number":"pith:7N5S4V7T","schema_version":"1.0","canonical_sha256":"fb7b2e57f3cb570cb1e47ea9d9fc415848e774f91ec36fc44c64838a9e6e59f2","source":{"kind":"arxiv","id":"2405.21052","version":1},"attestation_state":"computed","paper":{"title":"RydbergGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Benjamin MacLellan, David Fitzek, Ejaaz Merali, Gebremedhin A. Dagnew, Hin Pok Fung, M. Schuyler Moss, Roger G. Melko, Yi Hong Teoh","submitted_at":"2024-05-31T17:44:36Z","abstract_excerpt":"We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vicinity of a quantum phase transition in Rydberg atoms in a square lattice array. We explore the ability of the architecture to generalize, by producing groundstate measurements for Hamiltonian parameters not seen in the training set. We focu"},"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":"2405.21052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2024-05-31T17:44:36Z","cross_cats_sorted":[],"title_canon_sha256":"8f9887cceb54ace4bd1deb58731f958ba40e59a937b194e9af5052068f46263c","abstract_canon_sha256":"ee036f0f2f42a1282a3f2a44ec352165e50d74d2dd0a89e24d766ae29eb8d59b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:45.440724Z","signature_b64":"2ErL+nusQBM7YMT473w7dPloUP9SW4nE80tlsTv0pEqhbnSMZx84KGWeXG3RQ+L+bDsXMGR8kc1rmOLhBgHVCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb7b2e57f3cb570cb1e47ea9d9fc415848e774f91ec36fc44c64838a9e6e59f2","last_reissued_at":"2026-07-05T08:25:45.440232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:45.440232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RydbergGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Benjamin MacLellan, David Fitzek, Ejaaz Merali, Gebremedhin A. Dagnew, Hin Pok Fung, M. Schuyler Moss, Roger G. Melko, Yi Hong Teoh","submitted_at":"2024-05-31T17:44:36Z","abstract_excerpt":"We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vicinity of a quantum phase transition in Rydberg atoms in a square lattice array. We explore the ability of the architecture to generalize, by producing groundstate measurements for Hamiltonian parameters not seen in the training set. We focu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.21052","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/2405.21052/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":"2405.21052","created_at":"2026-07-05T08:25:45.440299+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.21052v1","created_at":"2026-07-05T08:25:45.440299+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.21052","created_at":"2026-07-05T08:25:45.440299+00:00"},{"alias_kind":"pith_short_12","alias_value":"7N5S4V7TZNLQ","created_at":"2026-07-05T08:25:45.440299+00:00"},{"alias_kind":"pith_short_16","alias_value":"7N5S4V7TZNLQZMPE","created_at":"2026-07-05T08:25:45.440299+00:00"},{"alias_kind":"pith_short_8","alias_value":"7N5S4V7T","created_at":"2026-07-05T08:25:45.440299+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16015","citing_title":"Discovering quantum phenomena with Interpretable Machine Learning","ref_index":61,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB","json":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB.json","graph_json":"https://pith.science/api/pith-number/7N5S4V7TZNLQZMPEP2U5T7CBLB/graph.json","events_json":"https://pith.science/api/pith-number/7N5S4V7TZNLQZMPEP2U5T7CBLB/events.json","paper":"https://pith.science/paper/7N5S4V7T"},"agent_actions":{"view_html":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB","download_json":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB.json","view_paper":"https://pith.science/paper/7N5S4V7T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.21052&json=true","fetch_graph":"https://pith.science/api/pith-number/7N5S4V7TZNLQZMPEP2U5T7CBLB/graph.json","fetch_events":"https://pith.science/api/pith-number/7N5S4V7TZNLQZMPEP2U5T7CBLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB/action/storage_attestation","attest_author":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB/action/author_attestation","sign_citation":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB/action/citation_signature","submit_replication":"https://pith.science/pith/7N5S4V7TZNLQZMPEP2U5T7CBLB/action/replication_record"}},"created_at":"2026-07-05T08:25:45.440299+00:00","updated_at":"2026-07-05T08:25:45.440299+00:00"}