{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OJZ4MBUYSGZSDNY4XZIPHBGCIG","short_pith_number":"pith:OJZ4MBUY","schema_version":"1.0","canonical_sha256":"7273c6069891b321b71cbe50f384c241a3247a36f3b1eef93db2edfe5fc9ca1e","source":{"kind":"arxiv","id":"2508.12822","version":1},"attestation_state":"computed","paper":{"title":"Graybox characterization and calibration with finite-shot estimation on superconducting-qubit experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Areeya Chantasri, Michal Hajdu\\v{s}ek, Poramet Pathumsoot, Rodney Van Meter","submitted_at":"2025-08-18T11:04:48Z","abstract_excerpt":"Characterization and calibration of quantum devices are necessary steps to achieve fault-tolerant quantum computing. As quantum devices become more sophisticated, it is increasingly essential to rely not only on physics-based models, but also on predictive models with open-loop optimization. Therefore, we choose the Graybox approach, which is composed of an explicit (whitebox) model describing the known dynamics and an implicit (blackbox) model describing the noisy dynamics in the form of a deep neural network, to characterize and calibrate superconducting-qubit devices. By sending a set of se"},"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":"2508.12822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-08-18T11:04:48Z","cross_cats_sorted":[],"title_canon_sha256":"30a402554985cf44f0c20adb1d792c32ef4acd94fd7d68ef2cb255928e7d0b39","abstract_canon_sha256":"8df420fc13469d326df35ba486c4fdec99bc9c6fb34504d1446c1fc659f45d89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:05:37.360572Z","signature_b64":"bY3PYwK9I2XEpBU5PANtSo5G9N2sKBZeO2WJ7/CVUNBwP6JqXUreLVOPQijR+Nehus8WyRmp60CvX6z/90pwBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7273c6069891b321b71cbe50f384c241a3247a36f3b1eef93db2edfe5fc9ca1e","last_reissued_at":"2026-05-27T01:05:37.359797Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:05:37.359797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graybox characterization and calibration with finite-shot estimation on superconducting-qubit experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Areeya Chantasri, Michal Hajdu\\v{s}ek, Poramet Pathumsoot, Rodney Van Meter","submitted_at":"2025-08-18T11:04:48Z","abstract_excerpt":"Characterization and calibration of quantum devices are necessary steps to achieve fault-tolerant quantum computing. As quantum devices become more sophisticated, it is increasingly essential to rely not only on physics-based models, but also on predictive models with open-loop optimization. Therefore, we choose the Graybox approach, which is composed of an explicit (whitebox) model describing the known dynamics and an implicit (blackbox) model describing the noisy dynamics in the form of a deep neural network, to characterize and calibrate superconducting-qubit devices. By sending a set of se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.12822","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/2508.12822/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":"2508.12822","created_at":"2026-05-27T01:05:37.359896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.12822v1","created_at":"2026-05-27T01:05:37.359896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.12822","created_at":"2026-05-27T01:05:37.359896+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJZ4MBUYSGZS","created_at":"2026-05-27T01:05:37.359896+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJZ4MBUYSGZSDNY4","created_at":"2026-05-27T01:05:37.359896+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJZ4MBUY","created_at":"2026-05-27T01:05:37.359896+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/OJZ4MBUYSGZSDNY4XZIPHBGCIG","json":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG.json","graph_json":"https://pith.science/api/pith-number/OJZ4MBUYSGZSDNY4XZIPHBGCIG/graph.json","events_json":"https://pith.science/api/pith-number/OJZ4MBUYSGZSDNY4XZIPHBGCIG/events.json","paper":"https://pith.science/paper/OJZ4MBUY"},"agent_actions":{"view_html":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG","download_json":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG.json","view_paper":"https://pith.science/paper/OJZ4MBUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.12822&json=true","fetch_graph":"https://pith.science/api/pith-number/OJZ4MBUYSGZSDNY4XZIPHBGCIG/graph.json","fetch_events":"https://pith.science/api/pith-number/OJZ4MBUYSGZSDNY4XZIPHBGCIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG/action/storage_attestation","attest_author":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG/action/author_attestation","sign_citation":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG/action/citation_signature","submit_replication":"https://pith.science/pith/OJZ4MBUYSGZSDNY4XZIPHBGCIG/action/replication_record"}},"created_at":"2026-05-27T01:05:37.359896+00:00","updated_at":"2026-05-27T01:05:37.359896+00:00"}