{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:K6BXB76QEHBCMVJWHFDFWALA2V","short_pith_number":"pith:K6BXB76Q","schema_version":"1.0","canonical_sha256":"578370ffd021c226553639465b0160d56f0517f585fcd2ed86dd0b56a05be933","source":{"kind":"arxiv","id":"2604.06265","version":2},"attestation_state":"computed","paper":{"title":"SMT-AD: a scalable quantum-inspired anomaly detection approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A quantum-inspired tensor network detects anomalies competitively with linear parameter growth.","cross_cats":["cond-mat.stat-mech","quant-ph"],"primary_cat":"cs.LG","authors_text":"Apimuk Sornsaeng, Dario Poletti, Jonathan Pan, Joshua Lim, Si Min Chan, Swee Liang Wong, Wenxuan Zhang","submitted_at":"2026-04-07T02:37:45Z","abstract_excerpt":"Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and the number of additional components in the "},"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":true},"canonical_record":{"source":{"id":"2604.06265","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-07T02:37:45Z","cross_cats_sorted":["cond-mat.stat-mech","quant-ph"],"title_canon_sha256":"a0b1ee2554d8461a3907bcaeac75cb1d22d064db56af83f9649d9cfc56688466","abstract_canon_sha256":"b043925d734af9aa42a639cc7187cd018ffd23d044e565ee811a336c1a8b4add"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:20.113215Z","signature_b64":"VwTtsQpWwrxAs7YifCi/jfWcIpVMJsKraY5HFz4gSBb1ggWukSu5MslZO0nGUDiorDBE1zCJJBq6JVVaWpw1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"578370ffd021c226553639465b0160d56f0517f585fcd2ed86dd0b56a05be933","last_reissued_at":"2026-06-19T16:12:20.112768Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:20.112768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SMT-AD: a scalable quantum-inspired anomaly detection approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A quantum-inspired tensor network detects anomalies competitively with linear parameter growth.","cross_cats":["cond-mat.stat-mech","quant-ph"],"primary_cat":"cs.LG","authors_text":"Apimuk Sornsaeng, Dario Poletti, Jonathan Pan, Joshua Lim, Si Min Chan, Swee Liang Wong, Wenxuan Zhang","submitted_at":"2026-04-07T02:37:45Z","abstract_excerpt":"Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and the number of additional components in the "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"even with minimal configurations, it achieves competitive performance against established anomaly detection baselines","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the superposition of bond-dimension-1 matrix product operators combined with Fourier-assisted multiresolution embedding can reliably separate anomalous from normal patterns in real data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SMT-AD applies superposition of bond-dimension-1 matrix product operators with multiresolution Fourier embedding to achieve competitive anomaly detection on standard datasets with linear parameter growth.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A quantum-inspired tensor network detects anomalies competitively with linear parameter growth.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d3b14bd0daf93ef710a2d729d2d74abba8ae313416371f3e36bd93b1801e49a9"},"source":{"id":"2604.06265","kind":"arxiv","version":2},"verdict":{"id":"ce3d4fbe-3582-4fd4-80af-49809c700ad6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T19:46:52.102278Z","strongest_claim":"even with minimal configurations, it achieves competitive performance against established anomaly detection baselines","one_line_summary":"SMT-AD applies superposition of bond-dimension-1 matrix product operators with multiresolution Fourier embedding to achieve competitive anomaly detection on standard datasets with linear parameter growth.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the superposition of bond-dimension-1 matrix product operators combined with Fourier-assisted multiresolution embedding can reliably separate anomalous from normal patterns in real data.","pith_extraction_headline":"A quantum-inspired tensor network detects anomalies competitively with linear parameter growth."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.06265/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":1,"snapshot_sha256":"7cfbf706b6ed019892ca32fddf10ea5182a668f41896aca6afc85fac12396f0a"},"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":"2604.06265","created_at":"2026-06-19T16:12:20.112839+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.06265v2","created_at":"2026-06-19T16:12:20.112839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.06265","created_at":"2026-06-19T16:12:20.112839+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6BXB76QEHBC","created_at":"2026-06-19T16:12:20.112839+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6BXB76QEHBCMVJW","created_at":"2026-06-19T16:12:20.112839+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6BXB76Q","created_at":"2026-06-19T16:12:20.112839+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V","json":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V.json","graph_json":"https://pith.science/api/pith-number/K6BXB76QEHBCMVJWHFDFWALA2V/graph.json","events_json":"https://pith.science/api/pith-number/K6BXB76QEHBCMVJWHFDFWALA2V/events.json","paper":"https://pith.science/paper/K6BXB76Q"},"agent_actions":{"view_html":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V","download_json":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V.json","view_paper":"https://pith.science/paper/K6BXB76Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.06265&json=true","fetch_graph":"https://pith.science/api/pith-number/K6BXB76QEHBCMVJWHFDFWALA2V/graph.json","fetch_events":"https://pith.science/api/pith-number/K6BXB76QEHBCMVJWHFDFWALA2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V/action/storage_attestation","attest_author":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V/action/author_attestation","sign_citation":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V/action/citation_signature","submit_replication":"https://pith.science/pith/K6BXB76QEHBCMVJWHFDFWALA2V/action/replication_record"}},"created_at":"2026-06-19T16:12:20.112839+00:00","updated_at":"2026-06-19T16:12:20.112839+00:00"}