{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:MMD7YNYR3ZRBREX2IQQCETETRM","short_pith_number":"pith:MMD7YNYR","canonical_record":{"source":{"id":"2206.13143","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-27T09:43:39Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"41f38e42a8e1cfe4541a83ea9264e69c7709eedf25a1826238cfe237fbdf8d0f","abstract_canon_sha256":"ca108afb221a92b91bd25be5a45ca7ed2a742ccb6f829f4d78e4abf4a2b03b6c"},"schema_version":"1.0"},"canonical_sha256":"6307fc3711de621892fa4420224c938b02818343ce506558d411467acbae08b4","source":{"kind":"arxiv","id":"2206.13143","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13143","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13143v3","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13143","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_12","alias_value":"MMD7YNYR3ZRB","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_16","alias_value":"MMD7YNYR3ZRBREX2","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_8","alias_value":"MMD7YNYR","created_at":"2026-07-05T06:05:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:MMD7YNYR3ZRBREX2IQQCETETRM","target":"record","payload":{"canonical_record":{"source":{"id":"2206.13143","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-27T09:43:39Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"41f38e42a8e1cfe4541a83ea9264e69c7709eedf25a1826238cfe237fbdf8d0f","abstract_canon_sha256":"ca108afb221a92b91bd25be5a45ca7ed2a742ccb6f829f4d78e4abf4a2b03b6c"},"schema_version":"1.0"},"canonical_sha256":"6307fc3711de621892fa4420224c938b02818343ce506558d411467acbae08b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:05:27.626796Z","signature_b64":"BwL4wb8abLsY8O+bqXU3iXrDQC5igh8meDWcCPjvHeoofmXUbJsYXQu+LJXcFANX2h4rX7GzwZkUZURXjQetAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6307fc3711de621892fa4420224c938b02818343ce506558d411467acbae08b4","last_reissued_at":"2026-07-05T06:05:27.626332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:05:27.626332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.13143","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:05:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rf+IVr8EAW66Hyp0VJKUPMtwEoZeBK+9hSJonGv1txLWgY+IPw7woAByio9QAyOvDj0P0gdpcDltgvue95tWBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:06:12.762613Z"},"content_sha256":"638474218fb6c48b29cc2f05394df92d7da0eecbf09fd5a3d2c362f6dcb3eb49","schema_version":"1.0","event_id":"sha256:638474218fb6c48b29cc2f05394df92d7da0eecbf09fd5a3d2c362f6dcb3eb49"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:MMD7YNYR3ZRBREX2IQQCETETRM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantum Regularized Least Squares","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"quant-ph","authors_text":"Aditya Morolia, Anurudh Peduri, Shantanav Chakraborty","submitted_at":"2022-06-27T09:43:39Z","abstract_excerpt":"Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear regression problems are often ill-posed or the underlying model suffers from overfitting, leading to erroneous or trivial solutions. This is often dealt with by adding extra constraints, known as regularization. In this paper, we use the frameworks of block-encoding and quantum singular value transformation (QSVT) to design the first quantum algorithms for quantum least squares with gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13143","kind":"arxiv","version":3},"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/2206.13143/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":"2026-07-05T06:05:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pYgxSrpH3ynXVf4qaiT99HyEGFWyZ+Mf1OxFSUktsdRitV/Kx65PbGLaHbC5sFuUQ6ikxLfcjoRT+t6wYEZHCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:06:12.763116Z"},"content_sha256":"ed2d0f95ac7a54ae3c51b055d18f1392651c8386003392642a29f67cc781ad8f","schema_version":"1.0","event_id":"sha256:ed2d0f95ac7a54ae3c51b055d18f1392651c8386003392642a29f67cc781ad8f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MMD7YNYR3ZRBREX2IQQCETETRM/bundle.json","state_url":"https://pith.science/pith/MMD7YNYR3ZRBREX2IQQCETETRM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MMD7YNYR3ZRBREX2IQQCETETRM/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":"2026-08-01T05:06:12Z","links":{"resolver":"https://pith.science/pith/MMD7YNYR3ZRBREX2IQQCETETRM","bundle":"https://pith.science/pith/MMD7YNYR3ZRBREX2IQQCETETRM/bundle.json","state":"https://pith.science/pith/MMD7YNYR3ZRBREX2IQQCETETRM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MMD7YNYR3ZRBREX2IQQCETETRM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MMD7YNYR3ZRBREX2IQQCETETRM","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":"ca108afb221a92b91bd25be5a45ca7ed2a742ccb6f829f4d78e4abf4a2b03b6c","cross_cats_sorted":["cs.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-27T09:43:39Z","title_canon_sha256":"41f38e42a8e1cfe4541a83ea9264e69c7709eedf25a1826238cfe237fbdf8d0f"},"schema_version":"1.0","source":{"id":"2206.13143","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13143","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13143v3","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13143","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_12","alias_value":"MMD7YNYR3ZRB","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_16","alias_value":"MMD7YNYR3ZRBREX2","created_at":"2026-07-05T06:05:27Z"},{"alias_kind":"pith_short_8","alias_value":"MMD7YNYR","created_at":"2026-07-05T06:05:27Z"}],"graph_snapshots":[{"event_id":"sha256:ed2d0f95ac7a54ae3c51b055d18f1392651c8386003392642a29f67cc781ad8f","target":"graph","created_at":"2026-07-05T06:05:27Z","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/2206.13143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear regression problems are often ill-posed or the underlying model suffers from overfitting, leading to erroneous or trivial solutions. This is often dealt with by adding extra constraints, known as regularization. In this paper, we use the frameworks of block-encoding and quantum singular value transformation (QSVT) to design the first quantum algorithms for quantum least squares with gene","authors_text":"Aditya Morolia, Anurudh Peduri, Shantanav Chakraborty","cross_cats":["cs.DS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-27T09:43:39Z","title":"Quantum Regularized Least Squares"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13143","kind":"arxiv","version":3},"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":"sha256:638474218fb6c48b29cc2f05394df92d7da0eecbf09fd5a3d2c362f6dcb3eb49","target":"record","created_at":"2026-07-05T06:05:27Z","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":"ca108afb221a92b91bd25be5a45ca7ed2a742ccb6f829f4d78e4abf4a2b03b6c","cross_cats_sorted":["cs.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-06-27T09:43:39Z","title_canon_sha256":"41f38e42a8e1cfe4541a83ea9264e69c7709eedf25a1826238cfe237fbdf8d0f"},"schema_version":"1.0","source":{"id":"2206.13143","kind":"arxiv","version":3}},"canonical_sha256":"6307fc3711de621892fa4420224c938b02818343ce506558d411467acbae08b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6307fc3711de621892fa4420224c938b02818343ce506558d411467acbae08b4","first_computed_at":"2026-07-05T06:05:27.626332Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:05:27.626332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BwL4wb8abLsY8O+bqXU3iXrDQC5igh8meDWcCPjvHeoofmXUbJsYXQu+LJXcFANX2h4rX7GzwZkUZURXjQetAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:05:27.626796Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.13143","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:638474218fb6c48b29cc2f05394df92d7da0eecbf09fd5a3d2c362f6dcb3eb49","sha256:ed2d0f95ac7a54ae3c51b055d18f1392651c8386003392642a29f67cc781ad8f"],"state_sha256":"c3bb4bfea155d3b4dfeb7368a948fc0c4be8c5fb4c1c4a8b1493023bc23cab02"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VWdd0rJFOYo5uhcDEYUjRYJBo1p0KiZBsHp81Zd+YN09YBb4sGRRVfIANWB2ynzlOGH8bpU57ILMHpAQBDWrCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T05:06:12.768135Z","bundle_sha256":"b25b9a70146c3f9559e7fdb2596a6039a43c750f2f95ba698ee22de7ec23585a"}}