{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MZIZUNGGKQDNOKCUTZN7AJD6HB","short_pith_number":"pith:MZIZUNGG","schema_version":"1.0","canonical_sha256":"66519a34c65406d728549e5bf0247e38463b3aac1304dec0c785c812373e8726","source":{"kind":"arxiv","id":"2405.04524","version":2},"attestation_state":"computed","paper":{"title":"Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall","quant-ph"],"primary_cat":"cond-mat.dis-nn","authors_text":"Jacob R. Taylor, Sankar Das Sarma","submitted_at":"2024-05-07T17:56:12Z","abstract_excerpt":"Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spi"},"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":"2405.04524","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2024-05-07T17:56:12Z","cross_cats_sorted":["cond-mat.mes-hall","quant-ph"],"title_canon_sha256":"5b1ccacaba35cf8d17bd58dcb08013fdfce23f54486deea158ebe138ac1b10d1","abstract_canon_sha256":"731703fe8988e1a57a7751f54a51970a85a51e05e21dedaddb6e4946d34ee6ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:46.126346Z","signature_b64":"HvQIfxtQrN98tSzIfUykjYXqQVNwYifvRePK7NAf5uO2cYEbRPjayzYBfLEy6L1+G4R8FmyRvlgOgXJWk9gaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66519a34c65406d728549e5bf0247e38463b3aac1304dec0c785c812373e8726","last_reissued_at":"2026-07-05T10:02:46.125837Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:46.125837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall","quant-ph"],"primary_cat":"cond-mat.dis-nn","authors_text":"Jacob R. Taylor, Sankar Das Sarma","submitted_at":"2024-05-07T17:56:12Z","abstract_excerpt":"Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04524","kind":"arxiv","version":2},"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/2405.04524/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":"2405.04524","created_at":"2026-07-05T10:02:46.125901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04524v2","created_at":"2026-07-05T10:02:46.125901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04524","created_at":"2026-07-05T10:02:46.125901+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZIZUNGGKQDN","created_at":"2026-07-05T10:02:46.125901+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZIZUNGGKQDNOKCU","created_at":"2026-07-05T10:02:46.125901+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZIZUNGG","created_at":"2026-07-05T10:02:46.125901+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.06768","citing_title":"Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB","json":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB.json","graph_json":"https://pith.science/api/pith-number/MZIZUNGGKQDNOKCUTZN7AJD6HB/graph.json","events_json":"https://pith.science/api/pith-number/MZIZUNGGKQDNOKCUTZN7AJD6HB/events.json","paper":"https://pith.science/paper/MZIZUNGG"},"agent_actions":{"view_html":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB","download_json":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB.json","view_paper":"https://pith.science/paper/MZIZUNGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04524&json=true","fetch_graph":"https://pith.science/api/pith-number/MZIZUNGGKQDNOKCUTZN7AJD6HB/graph.json","fetch_events":"https://pith.science/api/pith-number/MZIZUNGGKQDNOKCUTZN7AJD6HB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB/action/storage_attestation","attest_author":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB/action/author_attestation","sign_citation":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB/action/citation_signature","submit_replication":"https://pith.science/pith/MZIZUNGGKQDNOKCUTZN7AJD6HB/action/replication_record"}},"created_at":"2026-07-05T10:02:46.125901+00:00","updated_at":"2026-07-05T10:02:46.125901+00:00"}