{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CGNV5L52GWZA4TWR772GDE23E3","short_pith_number":"pith:CGNV5L52","schema_version":"1.0","canonical_sha256":"119b5eafba35b20e4ed1fff461935b26c8783ea2c4285616b947b06d6398556d","source":{"kind":"arxiv","id":"2607.02135","version":1},"attestation_state":"computed","paper":{"title":"Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Donovan Slabbert, Francesco Petruccione","submitted_at":"2026-07-02T13:11:31Z","abstract_excerpt":"Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterization and hierarchical representation of quantum information. Anomaly detection is an important machine learning task with applications across a wide range of domains, including scientific data analysis. In this work, we adapt a QCNN architecture into a quantum autoencoder (QAE) framework for reconstruction-based anomaly detection. The models are trained in a semi-supervised manner on normal samples to reconstruct feature-extracted and dimensionally"},"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":"2607.02135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-07-02T13:11:31Z","cross_cats_sorted":[],"title_canon_sha256":"d58e814533fadd3563ce7abcde9aac344a44f9362f70edc83887fded74c8e6c3","abstract_canon_sha256":"4886adf4140596e4db6dda4c9e8e63ab758a37f08b5e1eb3251afbf27ff84ab0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-03T01:17:43.128119Z","signature_b64":"pJUULpZt2p8blmdaxspeOE3nPILxQ+BTcialxSTeBdwxBEww3iFsBoEYSpvLe2GpIpaEzYNy9CM9kV7sfdzEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"119b5eafba35b20e4ed1fff461935b26c8783ea2c4285616b947b06d6398556d","last_reissued_at":"2026-07-03T01:17:43.127686Z","signature_status":"signed_v1","first_computed_at":"2026-07-03T01:17:43.127686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Donovan Slabbert, Francesco Petruccione","submitted_at":"2026-07-02T13:11:31Z","abstract_excerpt":"Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterization and hierarchical representation of quantum information. Anomaly detection is an important machine learning task with applications across a wide range of domains, including scientific data analysis. In this work, we adapt a QCNN architecture into a quantum autoencoder (QAE) framework for reconstruction-based anomaly detection. The models are trained in a semi-supervised manner on normal samples to reconstruct feature-extracted and dimensionally"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02135","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/2607.02135/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":"2607.02135","created_at":"2026-07-03T01:17:43.127746+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02135v1","created_at":"2026-07-03T01:17:43.127746+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02135","created_at":"2026-07-03T01:17:43.127746+00:00"},{"alias_kind":"pith_short_12","alias_value":"CGNV5L52GWZA","created_at":"2026-07-03T01:17:43.127746+00:00"},{"alias_kind":"pith_short_16","alias_value":"CGNV5L52GWZA4TWR","created_at":"2026-07-03T01:17:43.127746+00:00"},{"alias_kind":"pith_short_8","alias_value":"CGNV5L52","created_at":"2026-07-03T01:17:43.127746+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/CGNV5L52GWZA4TWR772GDE23E3","json":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3.json","graph_json":"https://pith.science/api/pith-number/CGNV5L52GWZA4TWR772GDE23E3/graph.json","events_json":"https://pith.science/api/pith-number/CGNV5L52GWZA4TWR772GDE23E3/events.json","paper":"https://pith.science/paper/CGNV5L52"},"agent_actions":{"view_html":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3","download_json":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3.json","view_paper":"https://pith.science/paper/CGNV5L52","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02135&json=true","fetch_graph":"https://pith.science/api/pith-number/CGNV5L52GWZA4TWR772GDE23E3/graph.json","fetch_events":"https://pith.science/api/pith-number/CGNV5L52GWZA4TWR772GDE23E3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3/action/storage_attestation","attest_author":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3/action/author_attestation","sign_citation":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3/action/citation_signature","submit_replication":"https://pith.science/pith/CGNV5L52GWZA4TWR772GDE23E3/action/replication_record"}},"created_at":"2026-07-03T01:17:43.127746+00:00","updated_at":"2026-07-03T01:17:43.127746+00:00"}