{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:BNO7BE4IQLOMHAKNWAYWSBXFFB","short_pith_number":"pith:BNO7BE4I","canonical_record":{"source":{"id":"2108.11670","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-08-26T09:36:20Z","cross_cats_sorted":[],"title_canon_sha256":"39c9aa26ecf8e3d6ba36e617d7143f9c78c8655f3b69dbb861c6a8de9eecaf80","abstract_canon_sha256":"ca4220ff6e7c22292c304f3eddf514da173f85c82654e1ea742f600ff76b76ab"},"schema_version":"1.0"},"canonical_sha256":"0b5df0938882dcc3814db0316906e52875289c109fc45a30dfce479315536b9f","source":{"kind":"arxiv","id":"2108.11670","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11670","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11670v5","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11670","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_12","alias_value":"BNO7BE4IQLOM","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_16","alias_value":"BNO7BE4IQLOMHAKN","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_8","alias_value":"BNO7BE4I","created_at":"2026-07-05T07:10:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:BNO7BE4IQLOMHAKNWAYWSBXFFB","target":"record","payload":{"canonical_record":{"source":{"id":"2108.11670","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-08-26T09:36:20Z","cross_cats_sorted":[],"title_canon_sha256":"39c9aa26ecf8e3d6ba36e617d7143f9c78c8655f3b69dbb861c6a8de9eecaf80","abstract_canon_sha256":"ca4220ff6e7c22292c304f3eddf514da173f85c82654e1ea742f600ff76b76ab"},"schema_version":"1.0"},"canonical_sha256":"0b5df0938882dcc3814db0316906e52875289c109fc45a30dfce479315536b9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:10:16.992963Z","signature_b64":"JgSONu4Szxl2m8t8/4ghnVCFKa5RLMxKkXmzo+ZR7mLVmBxiLqHWu/q7FzkxgJ/8ks9L7stMnIXUxrLY3iKBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b5df0938882dcc3814db0316906e52875289c109fc45a30dfce479315536b9f","last_reissued_at":"2026-07-05T07:10:16.992524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:10:16.992524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.11670","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:10:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mqGnW6HMPyDET55OgO+fOaRs/VsegEz6Lai4zfn4e+Ol6S9mtgFdaOYqOQzB+vJUkdKIj6HfNgdf4xi+bytqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:05:30.639820Z"},"content_sha256":"c63c1edf3befc1f236d5ec3497b372292c05f2956363efde068b1b626959f06d","schema_version":"1.0","event_id":"sha256:c63c1edf3befc1f236d5ec3497b372292c05f2956363efde068b1b626959f06d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:BNO7BE4IQLOMHAKNWAYWSBXFFB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantum Alphatron: quantum advantage for learning with kernels and noise","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Miklos Santha, Naixu Guo, Patrick Rebentrost, Siyi Yang","submitted_at":"2021-08-26T09:36:20Z","abstract_excerpt":"At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-accelerated time complexities. In the classical case, Klivans and Goel discussed the \\textit{Alphatron}, an algorithm to learn distributions related to kernelized regression, which they also applied to the learning of two-layer neural networks. In this work, we provide quantum versions of the Alphatron in the fault-tolerant setting. In a well-defined learning model, this quantum algorithm is able to provide a polynomia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11670","kind":"arxiv","version":5},"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/2108.11670/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:10:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QVcehZMwIOCRyrqpsA0ghWkzq4jfNqhD343bZ/6jEPWfPl6+KGWheLvEg1fHwYP9LLXgfHba3H5RdQ9Tl4opDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:05:30.640191Z"},"content_sha256":"771a75e08d34ba8e39ce45019bc667a6a99e394372cb70ba4daafd9a1626c679","schema_version":"1.0","event_id":"sha256:771a75e08d34ba8e39ce45019bc667a6a99e394372cb70ba4daafd9a1626c679"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/bundle.json","state_url":"https://pith.science/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-09T05:05:30Z","links":{"resolver":"https://pith.science/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB","bundle":"https://pith.science/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/bundle.json","state":"https://pith.science/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BNO7BE4IQLOMHAKNWAYWSBXFFB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:BNO7BE4IQLOMHAKNWAYWSBXFFB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ca4220ff6e7c22292c304f3eddf514da173f85c82654e1ea742f600ff76b76ab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-08-26T09:36:20Z","title_canon_sha256":"39c9aa26ecf8e3d6ba36e617d7143f9c78c8655f3b69dbb861c6a8de9eecaf80"},"schema_version":"1.0","source":{"id":"2108.11670","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11670","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11670v5","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11670","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_12","alias_value":"BNO7BE4IQLOM","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_16","alias_value":"BNO7BE4IQLOMHAKN","created_at":"2026-07-05T07:10:16Z"},{"alias_kind":"pith_short_8","alias_value":"BNO7BE4I","created_at":"2026-07-05T07:10:16Z"}],"graph_snapshots":[{"event_id":"sha256:771a75e08d34ba8e39ce45019bc667a6a99e394372cb70ba4daafd9a1626c679","target":"graph","created_at":"2026-07-05T07:10:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2108.11670/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-accelerated time complexities. In the classical case, Klivans and Goel discussed the \\textit{Alphatron}, an algorithm to learn distributions related to kernelized regression, which they also applied to the learning of two-layer neural networks. In this work, we provide quantum versions of the Alphatron in the fault-tolerant setting. In a well-defined learning model, this quantum algorithm is able to provide a polynomia","authors_text":"Miklos Santha, Naixu Guo, Patrick Rebentrost, Siyi Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-08-26T09:36:20Z","title":"Quantum Alphatron: quantum advantage for learning with kernels and noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11670","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c63c1edf3befc1f236d5ec3497b372292c05f2956363efde068b1b626959f06d","target":"record","created_at":"2026-07-05T07:10:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ca4220ff6e7c22292c304f3eddf514da173f85c82654e1ea742f600ff76b76ab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-08-26T09:36:20Z","title_canon_sha256":"39c9aa26ecf8e3d6ba36e617d7143f9c78c8655f3b69dbb861c6a8de9eecaf80"},"schema_version":"1.0","source":{"id":"2108.11670","kind":"arxiv","version":5}},"canonical_sha256":"0b5df0938882dcc3814db0316906e52875289c109fc45a30dfce479315536b9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b5df0938882dcc3814db0316906e52875289c109fc45a30dfce479315536b9f","first_computed_at":"2026-07-05T07:10:16.992524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:10:16.992524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JgSONu4Szxl2m8t8/4ghnVCFKa5RLMxKkXmzo+ZR7mLVmBxiLqHWu/q7FzkxgJ/8ks9L7stMnIXUxrLY3iKBDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:10:16.992963Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.11670","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c63c1edf3befc1f236d5ec3497b372292c05f2956363efde068b1b626959f06d","sha256:771a75e08d34ba8e39ce45019bc667a6a99e394372cb70ba4daafd9a1626c679"],"state_sha256":"3ed61adf2fc47bb50e1bafa704dec9e98785279448385e7b336aa57a2be33c2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2UbR6tijfWX1npZAoDTP3u980miIpW1UVWc9UK6GtWiEQmdsyI8vEKOeEVWn0mNvunUcfnIZ4Sm+x5+jdbGNCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:05:30.642158Z","bundle_sha256":"ea10c689304985acad41b5907fba0a89e46e4a7b8907cd5cb8b6a8aa1ec120cc"}}