{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:T4SH5NFJXLHFJV4ZNOI7ALYVZS","short_pith_number":"pith:T4SH5NFJ","schema_version":"1.0","canonical_sha256":"9f247eb4a9bace54d7996b91f02f15cc9fb395b027d7c9eae83a6f1cc0f7ee49","source":{"kind":"arxiv","id":"1902.00498","version":2},"attestation_state":"computed","paper":{"title":"Quantum Associative Memory in HEP Track Pattern Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"hep-ex","authors_text":"Illya Shapoval, Paolo Calafiura","submitted_at":"2019-01-30T01:23:03Z","abstract_excerpt":"We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data tri"},"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":"1902.00498","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2019-01-30T01:23:03Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"a28a45d9eab2b19288f009e0a0b8a28c534ac299736c9b8b35f6b5b3103b157f","abstract_canon_sha256":"3cdcd5797d129b236b65b886daceff41e5d771187a6774d436cd4f9ca729beab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:50.581629Z","signature_b64":"EUhoKWr/txm0jKNcK22EkDmTo72XlB1fF7gJ03dks1cRAmVTv/fl8Soe3O9S9645pyvi00VNnRj3hGOzkTf3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f247eb4a9bace54d7996b91f02f15cc9fb395b027d7c9eae83a6f1cc0f7ee49","last_reissued_at":"2026-07-05T00:18:50.581141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:50.581141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Associative Memory in HEP Track Pattern Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"hep-ex","authors_text":"Illya Shapoval, Paolo Calafiura","submitted_at":"2019-01-30T01:23:03Z","abstract_excerpt":"We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data tri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00498","kind":"arxiv","version":2},"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/1902.00498/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":"1902.00498","created_at":"2026-07-05T00:18:50.581198+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.00498v2","created_at":"2026-07-05T00:18:50.581198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.00498","created_at":"2026-07-05T00:18:50.581198+00:00"},{"alias_kind":"pith_short_12","alias_value":"T4SH5NFJXLHF","created_at":"2026-07-05T00:18:50.581198+00:00"},{"alias_kind":"pith_short_16","alias_value":"T4SH5NFJXLHFJV4Z","created_at":"2026-07-05T00:18:50.581198+00:00"},{"alias_kind":"pith_short_8","alias_value":"T4SH5NFJ","created_at":"2026-07-05T00:18:50.581198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.08949","citing_title":"Quantum Algorithms for Jet Clustering","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS","json":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS.json","graph_json":"https://pith.science/api/pith-number/T4SH5NFJXLHFJV4ZNOI7ALYVZS/graph.json","events_json":"https://pith.science/api/pith-number/T4SH5NFJXLHFJV4ZNOI7ALYVZS/events.json","paper":"https://pith.science/paper/T4SH5NFJ"},"agent_actions":{"view_html":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS","download_json":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS.json","view_paper":"https://pith.science/paper/T4SH5NFJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.00498&json=true","fetch_graph":"https://pith.science/api/pith-number/T4SH5NFJXLHFJV4ZNOI7ALYVZS/graph.json","fetch_events":"https://pith.science/api/pith-number/T4SH5NFJXLHFJV4ZNOI7ALYVZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS/action/storage_attestation","attest_author":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS/action/author_attestation","sign_citation":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS/action/citation_signature","submit_replication":"https://pith.science/pith/T4SH5NFJXLHFJV4ZNOI7ALYVZS/action/replication_record"}},"created_at":"2026-07-05T00:18:50.581198+00:00","updated_at":"2026-07-05T00:18:50.581198+00:00"}