{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:A2SXEJURNW63YLZOMN7XKU3DV5","short_pith_number":"pith:A2SXEJUR","schema_version":"1.0","canonical_sha256":"06a57226916dbdbc2f2e637f755363af44de24b19447ac99b4814d0f702748b4","source":{"kind":"arxiv","id":"2411.10487","version":3},"attestation_state":"computed","paper":{"title":"Architectural Patterns for Designing Quantum Artificial Intelligence Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.SE","authors_text":"Liming Zhu, Muhammad Usman, Mykhailo Klymenko, Qinghua Lu, Thong Hoang, Xiwei Xu, Zhenchang Xing","submitted_at":"2024-11-14T05:09:07Z","abstract_excerpt":"Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic"},"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":"2411.10487","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2024-11-14T05:09:07Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"7da3b8d72f304973846ff9053aeaa2c282b20b505b3c8533356e529f03b5fc3a","abstract_canon_sha256":"6f2aa2245da38a0f13238bb339ac36b7de607ffbe324cf13cf9e2dbcf27aa171"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:06.521999Z","signature_b64":"A+MDju8ShsOUlu8+VJ3f6Fd5zq5L4J+Ra2PZtxamrn0HQzBD6agPA/BnhIVXUja8cWtLMEXgw1Pnb1GluzkUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06a57226916dbdbc2f2e637f755363af44de24b19447ac99b4814d0f702748b4","last_reissued_at":"2026-07-05T09:50:06.521404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:06.521404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Architectural Patterns for Designing Quantum Artificial Intelligence Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.SE","authors_text":"Liming Zhu, Muhammad Usman, Mykhailo Klymenko, Qinghua Lu, Thong Hoang, Xiwei Xu, Zhenchang Xing","submitted_at":"2024-11-14T05:09:07Z","abstract_excerpt":"Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10487","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/2411.10487/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":"2411.10487","created_at":"2026-07-05T09:50:06.521469+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.10487v3","created_at":"2026-07-05T09:50:06.521469+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10487","created_at":"2026-07-05T09:50:06.521469+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2SXEJURNW63","created_at":"2026-07-05T09:50:06.521469+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2SXEJURNW63YLZO","created_at":"2026-07-05T09:50:06.521469+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2SXEJUR","created_at":"2026-07-05T09:50:06.521469+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17771","citing_title":"Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5","json":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5.json","graph_json":"https://pith.science/api/pith-number/A2SXEJURNW63YLZOMN7XKU3DV5/graph.json","events_json":"https://pith.science/api/pith-number/A2SXEJURNW63YLZOMN7XKU3DV5/events.json","paper":"https://pith.science/paper/A2SXEJUR"},"agent_actions":{"view_html":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5","download_json":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5.json","view_paper":"https://pith.science/paper/A2SXEJUR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.10487&json=true","fetch_graph":"https://pith.science/api/pith-number/A2SXEJURNW63YLZOMN7XKU3DV5/graph.json","fetch_events":"https://pith.science/api/pith-number/A2SXEJURNW63YLZOMN7XKU3DV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5/action/storage_attestation","attest_author":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5/action/author_attestation","sign_citation":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5/action/citation_signature","submit_replication":"https://pith.science/pith/A2SXEJURNW63YLZOMN7XKU3DV5/action/replication_record"}},"created_at":"2026-07-05T09:50:06.521469+00:00","updated_at":"2026-07-05T09:50:06.521469+00:00"}