{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:A372WJOXPWUM6I4NQIM7A6DVHU","short_pith_number":"pith:A372WJOX","schema_version":"1.0","canonical_sha256":"06ffab25d77da8cf238d8219f078753d2830f4337a6e9485be3dafd03e6952f1","source":{"kind":"arxiv","id":"1406.4203","version":1},"attestation_state":"computed","paper":{"title":"Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.LG","authors_text":"Hartmut Neven, Nan Ding, Ryan Babbush, Sergei Isakov, Vasil Denchev","submitted_at":"2014-06-17T00:53:59Z","abstract_excerpt":"Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system. To take advantage of a potential quantum advantage, one needs to be able to map the problem of interest to the native hardware with reasonably low overhead. Because experimental considerations constrain our objective function to take the form of a low degree PUBO (polynomial unconstrained binary optimization), we employ non-convex loss functions which are polynomial functions of the margin. We show that these loss"},"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":"1406.4203","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-17T00:53:59Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"089ac9824f4895fbaf1bb8b727db71d05048460bf79148af74214910cf24daa9","abstract_canon_sha256":"c617953fb81b998829159bea51f4c494d2b3774002984fbefed1daa56db3fe6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:49:38.186859Z","signature_b64":"lFErsBLhTmwR73IWQHioXHOXnRgUnXelRYZjF0iTHT00YM+eiAbXKsHBX5hBO6eQUBwpyCKMI7Y6j38PirekAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06ffab25d77da8cf238d8219f078753d2830f4337a6e9485be3dafd03e6952f1","last_reissued_at":"2026-05-18T02:49:38.186383Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:49:38.186383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.LG","authors_text":"Hartmut Neven, Nan Ding, Ryan Babbush, Sergei Isakov, Vasil Denchev","submitted_at":"2014-06-17T00:53:59Z","abstract_excerpt":"Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system. To take advantage of a potential quantum advantage, one needs to be able to map the problem of interest to the native hardware with reasonably low overhead. Because experimental considerations constrain our objective function to take the form of a low degree PUBO (polynomial unconstrained binary optimization), we employ non-convex loss functions which are polynomial functions of the margin. We show that these loss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.4203","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":""},"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":"1406.4203","created_at":"2026-05-18T02:49:38.186452+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.4203v1","created_at":"2026-05-18T02:49:38.186452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.4203","created_at":"2026-05-18T02:49:38.186452+00:00"},{"alias_kind":"pith_short_12","alias_value":"A372WJOXPWUM","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"A372WJOXPWUM6I4N","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"A372WJOX","created_at":"2026-05-18T12:28:19.803747+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/A372WJOXPWUM6I4NQIM7A6DVHU","json":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU.json","graph_json":"https://pith.science/api/pith-number/A372WJOXPWUM6I4NQIM7A6DVHU/graph.json","events_json":"https://pith.science/api/pith-number/A372WJOXPWUM6I4NQIM7A6DVHU/events.json","paper":"https://pith.science/paper/A372WJOX"},"agent_actions":{"view_html":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU","download_json":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU.json","view_paper":"https://pith.science/paper/A372WJOX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.4203&json=true","fetch_graph":"https://pith.science/api/pith-number/A372WJOXPWUM6I4NQIM7A6DVHU/graph.json","fetch_events":"https://pith.science/api/pith-number/A372WJOXPWUM6I4NQIM7A6DVHU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU/action/storage_attestation","attest_author":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU/action/author_attestation","sign_citation":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU/action/citation_signature","submit_replication":"https://pith.science/pith/A372WJOXPWUM6I4NQIM7A6DVHU/action/replication_record"}},"created_at":"2026-05-18T02:49:38.186452+00:00","updated_at":"2026-05-18T02:49:38.186452+00:00"}