{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:262WDHA72NFSJBWOQUU466VKWQ","short_pith_number":"pith:262WDHA7","schema_version":"1.0","canonical_sha256":"d7b5619c1fd34b2486ce8529cf7aaab413fd04bf06ef994ad471b71dcdaa81db","source":{"kind":"arxiv","id":"2104.07692","version":1},"attestation_state":"computed","paper":{"title":"Higgs analysis with quantum classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","physics.data-an"],"primary_cat":"quant-ph","authors_text":"Christina Reissel, El\\'ias F. Combarro, Florentin Reiter, G\\\"unther Dissertori, Samuel Gonz\\'alez-Castillo, Sofia Vallecorsa, Vasileios Belis","submitted_at":"2021-04-15T18:01:51Z","abstract_excerpt":"We have developed two quantum classifier models for the $t\\bar{t}H(b\\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -- to a"},"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":"2104.07692","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-15T18:01:51Z","cross_cats_sorted":["cs.LG","hep-ex","physics.data-an"],"title_canon_sha256":"575044efca06ee30b80da5f5aaf8f2f809db3dd410ca653b893703446e7f8ad4","abstract_canon_sha256":"3993c4c31d54662c4156845c54ed459b961ea61041c81a82034b9b72751e4ba4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:03.090929Z","signature_b64":"22wZKky3vhQxpQNgrP1Gqb8S/n8acc4gMRZ7p0kOQnrn6rBUUA3sXjkU3zK7X3amuL6j7B7h627c0e8x0jb4Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7b5619c1fd34b2486ce8529cf7aaab413fd04bf06ef994ad471b71dcdaa81db","last_reissued_at":"2026-07-05T03:12:03.090477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:03.090477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Higgs analysis with quantum classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","physics.data-an"],"primary_cat":"quant-ph","authors_text":"Christina Reissel, El\\'ias F. Combarro, Florentin Reiter, G\\\"unther Dissertori, Samuel Gonz\\'alez-Castillo, Sofia Vallecorsa, Vasileios Belis","submitted_at":"2021-04-15T18:01:51Z","abstract_excerpt":"We have developed two quantum classifier models for the $t\\bar{t}H(b\\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -- to a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.07692","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/2104.07692/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":"2104.07692","created_at":"2026-07-05T03:12:03.090540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.07692v1","created_at":"2026-07-05T03:12:03.090540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.07692","created_at":"2026-07-05T03:12:03.090540+00:00"},{"alias_kind":"pith_short_12","alias_value":"262WDHA72NFS","created_at":"2026-07-05T03:12:03.090540+00:00"},{"alias_kind":"pith_short_16","alias_value":"262WDHA72NFSJBWO","created_at":"2026-07-05T03:12:03.090540+00:00"},{"alias_kind":"pith_short_8","alias_value":"262WDHA7","created_at":"2026-07-05T03:12:03.090540+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01354","citing_title":"Local Conformal Predictions for Calibrated Surrogates","ref_index":158,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ","json":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ.json","graph_json":"https://pith.science/api/pith-number/262WDHA72NFSJBWOQUU466VKWQ/graph.json","events_json":"https://pith.science/api/pith-number/262WDHA72NFSJBWOQUU466VKWQ/events.json","paper":"https://pith.science/paper/262WDHA7"},"agent_actions":{"view_html":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ","download_json":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ.json","view_paper":"https://pith.science/paper/262WDHA7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.07692&json=true","fetch_graph":"https://pith.science/api/pith-number/262WDHA72NFSJBWOQUU466VKWQ/graph.json","fetch_events":"https://pith.science/api/pith-number/262WDHA72NFSJBWOQUU466VKWQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ/action/storage_attestation","attest_author":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ/action/author_attestation","sign_citation":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ/action/citation_signature","submit_replication":"https://pith.science/pith/262WDHA72NFSJBWOQUU466VKWQ/action/replication_record"}},"created_at":"2026-07-05T03:12:03.090540+00:00","updated_at":"2026-07-05T03:12:03.090540+00:00"}