{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WWYAUPYKH6KFGROEJ3RINFZLGA","short_pith_number":"pith:WWYAUPYK","schema_version":"1.0","canonical_sha256":"b5b00a3f0a3f945345c44ee286972b30194ff8dfb07df07bf4b1835029872fde","source":{"kind":"arxiv","id":"2607.20795","version":1},"attestation_state":"computed","paper":{"title":"Component-Level Inverse Design of Transmon Qubits Using Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Abhishek Chakraborty, Brucek Khailany, Daniel Baxter, Eli M Levenson-Falk, Enectali Figueroa-Feliciano, Firas Abouzahr, Haoyu Yang, Jonathan Asaadi, Nicola Pancotti, Olivia Seidel, Sadman Ahmed Shanto, Saikat Das, Sara Sussman, Taylor L. Patti","submitted_at":"2026-07-22T23:45:24Z","abstract_excerpt":"Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate 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":"2607.20795","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-07-22T23:45:24Z","cross_cats_sorted":[],"title_canon_sha256":"1f01e9e6122607b4b4193ac3072ecfe33c5a9baec94ca0ec4fcb17ba481f2675","abstract_canon_sha256":"3da72fadbbec306715d011af9ceb560b30c6147bb94bd0c9beb501e798ff4cdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-24T00:23:35.212296Z","signature_b64":"e7BS05eR8BThMwaQYuQI+0zEHkBStlyyISWmCmwX4+RaJSO72mpzrpiMtoTZEcQkfSs3Eg8Qah2GynQt0KwUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5b00a3f0a3f945345c44ee286972b30194ff8dfb07df07bf4b1835029872fde","last_reissued_at":"2026-07-24T00:23:35.211435Z","signature_status":"signed_v1","first_computed_at":"2026-07-24T00:23:35.211435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Component-Level Inverse Design of Transmon Qubits Using Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Abhishek Chakraborty, Brucek Khailany, Daniel Baxter, Eli M Levenson-Falk, Enectali Figueroa-Feliciano, Firas Abouzahr, Haoyu Yang, Jonathan Asaadi, Nicola Pancotti, Olivia Seidel, Sadman Ahmed Shanto, Saikat Das, Sara Sussman, Taylor L. Patti","submitted_at":"2026-07-22T23:45:24Z","abstract_excerpt":"Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20795","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/2607.20795/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":"2607.20795","created_at":"2026-07-24T00:23:35.211863+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.20795v1","created_at":"2026-07-24T00:23:35.211863+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20795","created_at":"2026-07-24T00:23:35.211863+00:00"},{"alias_kind":"pith_short_12","alias_value":"WWYAUPYKH6KF","created_at":"2026-07-24T00:23:35.211863+00:00"},{"alias_kind":"pith_short_16","alias_value":"WWYAUPYKH6KFGROE","created_at":"2026-07-24T00:23:35.211863+00:00"},{"alias_kind":"pith_short_8","alias_value":"WWYAUPYK","created_at":"2026-07-24T00:23:35.211863+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/WWYAUPYKH6KFGROEJ3RINFZLGA","json":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA.json","graph_json":"https://pith.science/api/pith-number/WWYAUPYKH6KFGROEJ3RINFZLGA/graph.json","events_json":"https://pith.science/api/pith-number/WWYAUPYKH6KFGROEJ3RINFZLGA/events.json","paper":"https://pith.science/paper/WWYAUPYK"},"agent_actions":{"view_html":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA","download_json":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA.json","view_paper":"https://pith.science/paper/WWYAUPYK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.20795&json=true","fetch_graph":"https://pith.science/api/pith-number/WWYAUPYKH6KFGROEJ3RINFZLGA/graph.json","fetch_events":"https://pith.science/api/pith-number/WWYAUPYKH6KFGROEJ3RINFZLGA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA/action/storage_attestation","attest_author":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA/action/author_attestation","sign_citation":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA/action/citation_signature","submit_replication":"https://pith.science/pith/WWYAUPYKH6KFGROEJ3RINFZLGA/action/replication_record"}},"created_at":"2026-07-24T00:23:35.211863+00:00","updated_at":"2026-07-24T00:23:35.211863+00:00"}