{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XTKQRIKSK4CRORPLQQGMLYQAEB","short_pith_number":"pith:XTKQRIKS","schema_version":"1.0","canonical_sha256":"bcd508a15257051745eb840cc5e200205af6093a7670fa6b5c73a050dbca1b5e","source":{"kind":"arxiv","id":"2508.14906","version":1},"attestation_state":"computed","paper":{"title":"Collaborative Filtering using Variational Quantum Hopfield Associative Memory","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.IR","authors_text":"Amir Kermanshahani, Ebrahim Ardeshir-Larijani, Rakesh Saini, Saif Al-Kuwari","submitted_at":"2025-08-12T07:33:11Z","abstract_excerpt":"Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machine Learning (QML), which integrates quantum computing with machine learning techniques, presents powerful new tools for data processing and pattern recognition. This paper proposes a hybrid recommendation system that combines Quantum Hopfield Associative Memory (QHAM) with deep neural networks to improve the extraction and classification on the MovieLens 1M dataset. User archety"},"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":"2508.14906","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2025-08-12T07:33:11Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG"],"title_canon_sha256":"ad6bf8aa985f8e342ec77ccc7161f08909e1323ef3a6190cc71b2d210c71fd95","abstract_canon_sha256":"ce1436ffbdb42c86a63deaa70ebaee61d530c370822ad0402db1c26905617aac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:03.095498Z","signature_b64":"xSj1jAsbLeYIMwo25YWlylmb9C0e/EM/BdeT0OUqYogd0nnUwaEZawYGiuaDIT4L0dJKi5nyV6FCY+Fuuu0vAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bcd508a15257051745eb840cc5e200205af6093a7670fa6b5c73a050dbca1b5e","last_reissued_at":"2026-07-05T11:57:03.095008Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:03.095008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Collaborative Filtering using Variational Quantum Hopfield Associative Memory","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.IR","authors_text":"Amir Kermanshahani, Ebrahim Ardeshir-Larijani, Rakesh Saini, Saif Al-Kuwari","submitted_at":"2025-08-12T07:33:11Z","abstract_excerpt":"Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machine Learning (QML), which integrates quantum computing with machine learning techniques, presents powerful new tools for data processing and pattern recognition. This paper proposes a hybrid recommendation system that combines Quantum Hopfield Associative Memory (QHAM) with deep neural networks to improve the extraction and classification on the MovieLens 1M dataset. User archety"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14906","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/2508.14906/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":"2508.14906","created_at":"2026-07-05T11:57:03.095064+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.14906v1","created_at":"2026-07-05T11:57:03.095064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14906","created_at":"2026-07-05T11:57:03.095064+00:00"},{"alias_kind":"pith_short_12","alias_value":"XTKQRIKSK4CR","created_at":"2026-07-05T11:57:03.095064+00:00"},{"alias_kind":"pith_short_16","alias_value":"XTKQRIKSK4CRORPL","created_at":"2026-07-05T11:57:03.095064+00:00"},{"alias_kind":"pith_short_8","alias_value":"XTKQRIKS","created_at":"2026-07-05T11:57:03.095064+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/XTKQRIKSK4CRORPLQQGMLYQAEB","json":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB.json","graph_json":"https://pith.science/api/pith-number/XTKQRIKSK4CRORPLQQGMLYQAEB/graph.json","events_json":"https://pith.science/api/pith-number/XTKQRIKSK4CRORPLQQGMLYQAEB/events.json","paper":"https://pith.science/paper/XTKQRIKS"},"agent_actions":{"view_html":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB","download_json":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB.json","view_paper":"https://pith.science/paper/XTKQRIKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.14906&json=true","fetch_graph":"https://pith.science/api/pith-number/XTKQRIKSK4CRORPLQQGMLYQAEB/graph.json","fetch_events":"https://pith.science/api/pith-number/XTKQRIKSK4CRORPLQQGMLYQAEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB/action/storage_attestation","attest_author":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB/action/author_attestation","sign_citation":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB/action/citation_signature","submit_replication":"https://pith.science/pith/XTKQRIKSK4CRORPLQQGMLYQAEB/action/replication_record"}},"created_at":"2026-07-05T11:57:03.095064+00:00","updated_at":"2026-07-05T11:57:03.095064+00:00"}