{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JZ4M2OT2ZVEAPKOZNCHEGQ2OLI","short_pith_number":"pith:JZ4M2OT2","schema_version":"1.0","canonical_sha256":"4e78cd3a7acd4807a9d9688e43434e5a096ce0c147c397bb869ee198c440fff9","source":{"kind":"arxiv","id":"2507.14031","version":1},"attestation_state":"computed","paper":{"title":"QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Fang, Hao Yu, Huaiwu He, Sihao Teng, Siyi Yuan, Yunjie Yang, Zhe Liu","submitted_at":"2025-07-18T15:57:53Z","abstract_excerpt":"Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum-Assisted Inference (QuantEIT) framework for EIT image reconstruction. QuantEIT leverages a Quantum-A"},"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":"2507.14031","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-18T15:57:53Z","cross_cats_sorted":["cs.ET","cs.LG"],"title_canon_sha256":"7f025c607dac89ddc5eb6d365b32fbba78aa7e860ed7efe308f4578fec02fab2","abstract_canon_sha256":"fb8814150acb6d7ddd733bc47b34a586b24a67a15e18ec65867c8a47d0b59d77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:27.343550Z","signature_b64":"AYqV5mkMf3am6sJYGKxzrTwtwzxtCRA/GaFFQFxbb33rfuloGPLhxdl+R5xfBtOwKqk8jZbTp59L7i6OqEH/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e78cd3a7acd4807a9d9688e43434e5a096ce0c147c397bb869ee198c440fff9","last_reissued_at":"2026-07-05T11:39:27.343061Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:27.343061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Fang, Hao Yu, Huaiwu He, Sihao Teng, Siyi Yuan, Yunjie Yang, Zhe Liu","submitted_at":"2025-07-18T15:57:53Z","abstract_excerpt":"Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum-Assisted Inference (QuantEIT) framework for EIT image reconstruction. QuantEIT leverages a Quantum-A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14031","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/2507.14031/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":"2507.14031","created_at":"2026-07-05T11:39:27.343118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.14031v1","created_at":"2026-07-05T11:39:27.343118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14031","created_at":"2026-07-05T11:39:27.343118+00:00"},{"alias_kind":"pith_short_12","alias_value":"JZ4M2OT2ZVEA","created_at":"2026-07-05T11:39:27.343118+00:00"},{"alias_kind":"pith_short_16","alias_value":"JZ4M2OT2ZVEAPKOZ","created_at":"2026-07-05T11:39:27.343118+00:00"},{"alias_kind":"pith_short_8","alias_value":"JZ4M2OT2","created_at":"2026-07-05T11:39:27.343118+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/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI","json":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI.json","graph_json":"https://pith.science/api/pith-number/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/graph.json","events_json":"https://pith.science/api/pith-number/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/events.json","paper":"https://pith.science/paper/JZ4M2OT2"},"agent_actions":{"view_html":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI","download_json":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI.json","view_paper":"https://pith.science/paper/JZ4M2OT2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.14031&json=true","fetch_graph":"https://pith.science/api/pith-number/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/graph.json","fetch_events":"https://pith.science/api/pith-number/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/action/storage_attestation","attest_author":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/action/author_attestation","sign_citation":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/action/citation_signature","submit_replication":"https://pith.science/pith/JZ4M2OT2ZVEAPKOZNCHEGQ2OLI/action/replication_record"}},"created_at":"2026-07-05T11:39:27.343118+00:00","updated_at":"2026-07-05T11:39:27.343118+00:00"}