{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PATIHGHR5PIIRLC2H2446CGC2H","short_pith_number":"pith:PATIHGHR","schema_version":"1.0","canonical_sha256":"78268398f1ebd088ac5a3eb9cf08c2d1feb76b8dff40b3437e346d7f3d55d61e","source":{"kind":"arxiv","id":"2406.05175","version":3},"attestation_state":"computed","paper":{"title":"Robust quantum dots charge autotuning using neural network uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mes-hall","cs.LG"],"primary_cat":"quant-ph","authors_text":"Bastien Galaup, Claude Rohrbacher, Cl\\'ement Godfrin, Dominique Drouin, Eva Dupont-Ferrier, Joffrey Rivard, Kristiaan De Greve, Louis Gaudreau, Roger G. Melko, Ruoyu Li, Stefan Kubicek, Victor Yon, Yann Beilliard","submitted_at":"2024-06-07T16:33:23Z","abstract_excerpt":"This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in op"},"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":"2406.05175","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2024-06-07T16:33:23Z","cross_cats_sorted":["cond-mat.mes-hall","cs.LG"],"title_canon_sha256":"9f40827342500e5a94d5199242840c20c8bafcc278885b307cc9ce6d63c5b458","abstract_canon_sha256":"9e9a0951d5d13686e2ecf5bdf27304768634b1c541558cca0a7b2b4df1f20e60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:42.108447Z","signature_b64":"+ljvFLQNxKo80dnoVNKXwNvF26hql0yxElYDELNadrN+4qMQ4vNvfVDmoIJrwvexcz74cKr3ZHtCE6dCfIsnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78268398f1ebd088ac5a3eb9cf08c2d1feb76b8dff40b3437e346d7f3d55d61e","last_reissued_at":"2026-07-05T10:07:42.107892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:42.107892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust quantum dots charge autotuning using neural network uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mes-hall","cs.LG"],"primary_cat":"quant-ph","authors_text":"Bastien Galaup, Claude Rohrbacher, Cl\\'ement Godfrin, Dominique Drouin, Eva Dupont-Ferrier, Joffrey Rivard, Kristiaan De Greve, Louis Gaudreau, Roger G. Melko, Ruoyu Li, Stefan Kubicek, Victor Yon, Yann Beilliard","submitted_at":"2024-06-07T16:33:23Z","abstract_excerpt":"This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05175","kind":"arxiv","version":3},"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/2406.05175/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":"2406.05175","created_at":"2026-07-05T10:07:42.107949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05175v3","created_at":"2026-07-05T10:07:42.107949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05175","created_at":"2026-07-05T10:07:42.107949+00:00"},{"alias_kind":"pith_short_12","alias_value":"PATIHGHR5PII","created_at":"2026-07-05T10:07:42.107949+00:00"},{"alias_kind":"pith_short_16","alias_value":"PATIHGHR5PIIRLC2","created_at":"2026-07-05T10:07:42.107949+00:00"},{"alias_kind":"pith_short_8","alias_value":"PATIHGHR","created_at":"2026-07-05T10:07:42.107949+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/PATIHGHR5PIIRLC2H2446CGC2H","json":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H.json","graph_json":"https://pith.science/api/pith-number/PATIHGHR5PIIRLC2H2446CGC2H/graph.json","events_json":"https://pith.science/api/pith-number/PATIHGHR5PIIRLC2H2446CGC2H/events.json","paper":"https://pith.science/paper/PATIHGHR"},"agent_actions":{"view_html":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H","download_json":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H.json","view_paper":"https://pith.science/paper/PATIHGHR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05175&json=true","fetch_graph":"https://pith.science/api/pith-number/PATIHGHR5PIIRLC2H2446CGC2H/graph.json","fetch_events":"https://pith.science/api/pith-number/PATIHGHR5PIIRLC2H2446CGC2H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H/action/storage_attestation","attest_author":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H/action/author_attestation","sign_citation":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H/action/citation_signature","submit_replication":"https://pith.science/pith/PATIHGHR5PIIRLC2H2446CGC2H/action/replication_record"}},"created_at":"2026-07-05T10:07:42.107949+00:00","updated_at":"2026-07-05T10:07:42.107949+00:00"}