{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:DR7ZZ56LZ3K4DTWQFUVFQFAX62","short_pith_number":"pith:DR7ZZ56L","canonical_record":{"source":{"id":"1905.00365","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2019-05-01T16:29:04Z","cross_cats_sorted":["cs.LG","quant-ph"],"title_canon_sha256":"621391718f80f9db8425e5ed3d4a17991be7afcf791312f207b13c6976093cf0","abstract_canon_sha256":"1e68f5ed301cc4211db6860b9f11f8aa10d27e12e9bddf5363154c49e9ab4a97"},"schema_version":"1.0"},"canonical_sha256":"1c7f9cf7cbced5c1ced02d2a581417f6bb1244f9454878c96ff615ac60dd923d","source":{"kind":"arxiv","id":"1905.00365","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00365","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00365v1","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00365","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"pith_short_12","alias_value":"DR7ZZ56LZ3K4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DR7ZZ56LZ3K4DTWQ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DR7ZZ56L","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:DR7ZZ56LZ3K4DTWQFUVFQFAX62","target":"record","payload":{"canonical_record":{"source":{"id":"1905.00365","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2019-05-01T16:29:04Z","cross_cats_sorted":["cs.LG","quant-ph"],"title_canon_sha256":"621391718f80f9db8425e5ed3d4a17991be7afcf791312f207b13c6976093cf0","abstract_canon_sha256":"1e68f5ed301cc4211db6860b9f11f8aa10d27e12e9bddf5363154c49e9ab4a97"},"schema_version":"1.0"},"canonical_sha256":"1c7f9cf7cbced5c1ced02d2a581417f6bb1244f9454878c96ff615ac60dd923d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:13.061643Z","signature_b64":"/gLmXke3fLXcdtK4PakL4aHh8AyVd2ElK37JqxLD+NyNvM6JgVimq+3SVzyh5vrMwnBXx+Rmhc3r3Zo2QVhFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c7f9cf7cbced5c1ced02d2a581417f6bb1244f9454878c96ff615ac60dd923d","last_reissued_at":"2026-05-17T23:47:13.061173Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:13.061173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.00365","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GbQ4d3YPoFrW0ctyrTEn83JbcR0eb8wjriwASv36B4aIpybJhRDHt4H1FEzlrr48W0aBF1lAqsWlTWoLymShCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:00:17.655260Z"},"content_sha256":"4c5c7c9105e4a4b247d97590be95583359a858ae74396c7da6f20aa9dcdcc5f7","schema_version":"1.0","event_id":"sha256:4c5c7c9105e4a4b247d97590be95583359a858ae74396c7da6f20aa9dcdcc5f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:DR7ZZ56LZ3K4DTWQFUVFQFAX62","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantum Generalized Linear Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","quant-ph"],"primary_cat":"stat.ME","authors_text":"Colleen M. Farrelly, Srikanth Namuduri, Uchenna Chukwu","submitted_at":"2019-05-01T16:29:04Z","abstract_excerpt":"Generalized linear models (GLM) are link function based statistical models. Many supervised learning algorithms are extensions of GLMs and have link functions built into the algorithm to model different outcome distributions. There are two major drawbacks when using this approach in applications using real world datasets. One is that none of the link functions available in the popular packages is a good fit for the data. Second, it is computationally inefficient and impractical to test all the possible distributions to find the optimum one. In addition, many GLMs and their machine learning ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00365","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NL+J/0JVkSOaFN284aKqmdoUMuTztWOTG+PL2lPQ3Pi4e5gKpMxBy4mSwYI2zITOYzZwGq8dZTmtmjfLfR5vAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:00:17.655784Z"},"content_sha256":"2cef001e11b1786d7c52ca3aa4510fa1cb90ec5eb7ca236b751c637732064c02","schema_version":"1.0","event_id":"sha256:2cef001e11b1786d7c52ca3aa4510fa1cb90ec5eb7ca236b751c637732064c02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/bundle.json","state_url":"https://pith.science/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/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":"2026-07-05T12:00:17Z","links":{"resolver":"https://pith.science/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62","bundle":"https://pith.science/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/bundle.json","state":"https://pith.science/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DR7ZZ56LZ3K4DTWQFUVFQFAX62/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:DR7ZZ56LZ3K4DTWQFUVFQFAX62","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":"1e68f5ed301cc4211db6860b9f11f8aa10d27e12e9bddf5363154c49e9ab4a97","cross_cats_sorted":["cs.LG","quant-ph"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2019-05-01T16:29:04Z","title_canon_sha256":"621391718f80f9db8425e5ed3d4a17991be7afcf791312f207b13c6976093cf0"},"schema_version":"1.0","source":{"id":"1905.00365","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00365","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00365v1","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00365","created_at":"2026-05-17T23:47:13Z"},{"alias_kind":"pith_short_12","alias_value":"DR7ZZ56LZ3K4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DR7ZZ56LZ3K4DTWQ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DR7ZZ56L","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:2cef001e11b1786d7c52ca3aa4510fa1cb90ec5eb7ca236b751c637732064c02","target":"graph","created_at":"2026-05-17T23:47:13Z","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"},"paper":{"abstract_excerpt":"Generalized linear models (GLM) are link function based statistical models. Many supervised learning algorithms are extensions of GLMs and have link functions built into the algorithm to model different outcome distributions. There are two major drawbacks when using this approach in applications using real world datasets. One is that none of the link functions available in the popular packages is a good fit for the data. Second, it is computationally inefficient and impractical to test all the possible distributions to find the optimum one. In addition, many GLMs and their machine learning ext","authors_text":"Colleen M. Farrelly, Srikanth Namuduri, Uchenna Chukwu","cross_cats":["cs.LG","quant-ph"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2019-05-01T16:29:04Z","title":"Quantum Generalized Linear Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00365","kind":"arxiv","version":1},"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":"sha256:4c5c7c9105e4a4b247d97590be95583359a858ae74396c7da6f20aa9dcdcc5f7","target":"record","created_at":"2026-05-17T23:47:13Z","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":"1e68f5ed301cc4211db6860b9f11f8aa10d27e12e9bddf5363154c49e9ab4a97","cross_cats_sorted":["cs.LG","quant-ph"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2019-05-01T16:29:04Z","title_canon_sha256":"621391718f80f9db8425e5ed3d4a17991be7afcf791312f207b13c6976093cf0"},"schema_version":"1.0","source":{"id":"1905.00365","kind":"arxiv","version":1}},"canonical_sha256":"1c7f9cf7cbced5c1ced02d2a581417f6bb1244f9454878c96ff615ac60dd923d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c7f9cf7cbced5c1ced02d2a581417f6bb1244f9454878c96ff615ac60dd923d","first_computed_at":"2026-05-17T23:47:13.061173Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:13.061173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/gLmXke3fLXcdtK4PakL4aHh8AyVd2ElK37JqxLD+NyNvM6JgVimq+3SVzyh5vrMwnBXx+Rmhc3r3Zo2QVhFBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:13.061643Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.00365","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c5c7c9105e4a4b247d97590be95583359a858ae74396c7da6f20aa9dcdcc5f7","sha256:2cef001e11b1786d7c52ca3aa4510fa1cb90ec5eb7ca236b751c637732064c02"],"state_sha256":"8f3a378551ced1a70cc43d3194c8f7a6c98dd9c1dfdf0b148369c2dae8206069"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+HYr3y1pyhq6TdvIG1x3YBKGun8ZIWUUIOUF2G4uvFmjsICgeF4Ak9Vwv0q8F/FmAOPF5eaiVWE5EM2opQ/aCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T12:00:17.657669Z","bundle_sha256":"662b13d44256d61407a8117e5b71f23fb423d567271e67c37cee111fcaba9eb6"}}