{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:G7VJP7LBAWECPRDIOBKRBQNZNN","short_pith_number":"pith:G7VJP7LB","schema_version":"1.0","canonical_sha256":"37ea97fd61058827c468705510c1b96b4fb9deb81be17805b7654e4a8e4d9d74","source":{"kind":"arxiv","id":"1711.06652","version":1},"attestation_state":"computed","paper":{"title":"Hardening Quantum Machine Learning Against Adversaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"quant-ph","authors_text":"Nathan Wiebe, Ram Shankar Siva Kumar","submitted_at":"2017-11-17T18:02:26Z","abstract_excerpt":"Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help make quantum classifiers more secure or private. In particular, we demonstrate a form of robust principal component analysis that, under some circumstances, can provide an exponential speedup relative to robust methods used at present. To demonstrate this approach we introduce a linear combinations"},"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":"1711.06652","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2017-11-17T18:02:26Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"b660a98cbf3d4873e1a8191de674b6f65dd764e13b0d5520eb37ec35e940a820","abstract_canon_sha256":"13afae2185cebd519fca1fb6393626151dcd88a9fe1458e25adf6e216347ee17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:18.304251Z","signature_b64":"+bAqZTJoU6IfD1MDEo2Dx4m6YKOGVtIuqA6X4fmerB4A4feCQHVbHEeZGx8BnVEEM+2KXG6sFFl+qePHlj1lDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37ea97fd61058827c468705510c1b96b4fb9deb81be17805b7654e4a8e4d9d74","last_reissued_at":"2026-05-18T00:30:18.303469Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:18.303469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hardening Quantum Machine Learning Against Adversaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"quant-ph","authors_text":"Nathan Wiebe, Ram Shankar Siva Kumar","submitted_at":"2017-11-17T18:02:26Z","abstract_excerpt":"Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help make quantum classifiers more secure or private. In particular, we demonstrate a form of robust principal component analysis that, under some circumstances, can provide an exponential speedup relative to robust methods used at present. To demonstrate this approach we introduce a linear combinations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06652","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":"1711.06652","created_at":"2026-05-18T00:30:18.303592+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.06652v1","created_at":"2026-05-18T00:30:18.303592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06652","created_at":"2026-05-18T00:30:18.303592+00:00"},{"alias_kind":"pith_short_12","alias_value":"G7VJP7LBAWEC","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"G7VJP7LBAWECPRDI","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"G7VJP7LB","created_at":"2026-05-18T12:31:15.632608+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/G7VJP7LBAWECPRDIOBKRBQNZNN","json":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN.json","graph_json":"https://pith.science/api/pith-number/G7VJP7LBAWECPRDIOBKRBQNZNN/graph.json","events_json":"https://pith.science/api/pith-number/G7VJP7LBAWECPRDIOBKRBQNZNN/events.json","paper":"https://pith.science/paper/G7VJP7LB"},"agent_actions":{"view_html":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN","download_json":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN.json","view_paper":"https://pith.science/paper/G7VJP7LB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.06652&json=true","fetch_graph":"https://pith.science/api/pith-number/G7VJP7LBAWECPRDIOBKRBQNZNN/graph.json","fetch_events":"https://pith.science/api/pith-number/G7VJP7LBAWECPRDIOBKRBQNZNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN/action/storage_attestation","attest_author":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN/action/author_attestation","sign_citation":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN/action/citation_signature","submit_replication":"https://pith.science/pith/G7VJP7LBAWECPRDIOBKRBQNZNN/action/replication_record"}},"created_at":"2026-05-18T00:30:18.303592+00:00","updated_at":"2026-05-18T00:30:18.303592+00:00"}