{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IQFNCSIXFQLIASA77P2W2HTB22","short_pith_number":"pith:IQFNCSIX","schema_version":"1.0","canonical_sha256":"440ad149172c1680481ffbf56d1e61d68290d81435fd709eb1328aaadd3913fe","source":{"kind":"arxiv","id":"2010.02174","version":2},"attestation_state":"computed","paper":{"title":"A rigorous and robust quantum speed-up in supervised machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Kristan Temme, Srinivasan Arunachalam, Yunchao Liu","submitted_at":"2020-10-05T17:22:22Z","abstract_excerpt":"Over the past few years several quantum machine learning algorithms were proposed that promise quantum speed-ups over their classical counterparts. Most of these learning algorithms either assume quantum access to data -- making it unclear if quantum speed-ups still exist without making these strong assumptions, or are heuristic in nature with no provable advantage over classical algorithms. In this paper, we establish a rigorous quantum speed-up for supervised classification using a general-purpose quantum learning algorithm that only requires classical access to data. Our quantum classifier "},"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":"2010.02174","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2020-10-05T17:22:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6e7908d6baeb29e22a6716fbbc540072a2511550f3c7058821c75315e09641de","abstract_canon_sha256":"92ead300f32d3f8230b98d04a2e37548fc945c4dafde06ecf7e0f202fea678d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:09.407864Z","signature_b64":"nnMrOMOW8i+dFZW3bFk81I1j/BYo5XQUhLpFTeVfr3UVg+iQpljqNShVY9/XPdaoK1F7f7i0u2tFXdSY1sggAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"440ad149172c1680481ffbf56d1e61d68290d81435fd709eb1328aaadd3913fe","last_reissued_at":"2026-07-05T02:57:09.407485Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:09.407485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A rigorous and robust quantum speed-up in supervised machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Kristan Temme, Srinivasan Arunachalam, Yunchao Liu","submitted_at":"2020-10-05T17:22:22Z","abstract_excerpt":"Over the past few years several quantum machine learning algorithms were proposed that promise quantum speed-ups over their classical counterparts. Most of these learning algorithms either assume quantum access to data -- making it unclear if quantum speed-ups still exist without making these strong assumptions, or are heuristic in nature with no provable advantage over classical algorithms. In this paper, we establish a rigorous quantum speed-up for supervised classification using a general-purpose quantum learning algorithm that only requires classical access to data. Our quantum classifier "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02174","kind":"arxiv","version":2},"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/2010.02174/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":"2010.02174","created_at":"2026-07-05T02:57:09.407543+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.02174v2","created_at":"2026-07-05T02:57:09.407543+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02174","created_at":"2026-07-05T02:57:09.407543+00:00"},{"alias_kind":"pith_short_12","alias_value":"IQFNCSIXFQLI","created_at":"2026-07-05T02:57:09.407543+00:00"},{"alias_kind":"pith_short_16","alias_value":"IQFNCSIXFQLIASA7","created_at":"2026-07-05T02:57:09.407543+00:00"},{"alias_kind":"pith_short_8","alias_value":"IQFNCSIX","created_at":"2026-07-05T02:57:09.407543+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28169","citing_title":"Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21276","citing_title":"Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22","json":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22.json","graph_json":"https://pith.science/api/pith-number/IQFNCSIXFQLIASA77P2W2HTB22/graph.json","events_json":"https://pith.science/api/pith-number/IQFNCSIXFQLIASA77P2W2HTB22/events.json","paper":"https://pith.science/paper/IQFNCSIX"},"agent_actions":{"view_html":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22","download_json":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22.json","view_paper":"https://pith.science/paper/IQFNCSIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.02174&json=true","fetch_graph":"https://pith.science/api/pith-number/IQFNCSIXFQLIASA77P2W2HTB22/graph.json","fetch_events":"https://pith.science/api/pith-number/IQFNCSIXFQLIASA77P2W2HTB22/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22/action/storage_attestation","attest_author":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22/action/author_attestation","sign_citation":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22/action/citation_signature","submit_replication":"https://pith.science/pith/IQFNCSIXFQLIASA77P2W2HTB22/action/replication_record"}},"created_at":"2026-07-05T02:57:09.407543+00:00","updated_at":"2026-07-05T02:57:09.407543+00:00"}