{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:J3GUZQLTLHAGJ5HQ2P7AG4SBZ6","short_pith_number":"pith:J3GUZQLT","canonical_record":{"source":{"id":"2505.08759","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-13T17:28:08Z","cross_cats_sorted":[],"title_canon_sha256":"3c916d78261d1ce534f7e9d02c015313b1a3a21c15560245d98495eb2a8f9bc5","abstract_canon_sha256":"2677efd68a9f2aafcb13d0078ec4bd00cb48f6261097503a8cf1971c8c75d2bd"},"schema_version":"1.0"},"canonical_sha256":"4ecd4cc17359c064f4f0d3fe037241cfbc0e0de60fa0d3cc15c06bc13ecf8180","source":{"kind":"arxiv","id":"2505.08759","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08759","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08759v1","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08759","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_12","alias_value":"J3GUZQLTLHAG","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_16","alias_value":"J3GUZQLTLHAGJ5HQ","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_8","alias_value":"J3GUZQLT","created_at":"2026-06-09T01:05:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:J3GUZQLTLHAGJ5HQ2P7AG4SBZ6","target":"record","payload":{"canonical_record":{"source":{"id":"2505.08759","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-13T17:28:08Z","cross_cats_sorted":[],"title_canon_sha256":"3c916d78261d1ce534f7e9d02c015313b1a3a21c15560245d98495eb2a8f9bc5","abstract_canon_sha256":"2677efd68a9f2aafcb13d0078ec4bd00cb48f6261097503a8cf1971c8c75d2bd"},"schema_version":"1.0"},"canonical_sha256":"4ecd4cc17359c064f4f0d3fe037241cfbc0e0de60fa0d3cc15c06bc13ecf8180","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:06.564798Z","signature_b64":"pbTuE1PMboEp4fvttY2AXdU0syCVCQ5w02ojn6uQENT4HZIz6G/w4aWAqNcbYnCS/e9No9WXtlo0hQd48c4pAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ecd4cc17359c064f4f0d3fe037241cfbc0e0de60fa0d3cc15c06bc13ecf8180","last_reissued_at":"2026-06-09T01:05:06.564270Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:06.564270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.08759","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":"2026-06-09T01:05:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PzRv3qc9SpyRgZAKhn/8xEj0dxVJm72apVHjpu/GzBRPnVIK31T4YK+ipGrmA5mgOzNDfIr4K5TFd+unPVwQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T00:52:59.094493Z"},"content_sha256":"62a8f192db917813c1414594c77edb0824d28b17bf99e807fb45cdb8cd58cee5","schema_version":"1.0","event_id":"sha256:62a8f192db917813c1414594c77edb0824d28b17bf99e807fb45cdb8cd58cee5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:J3GUZQLTLHAGJ5HQ2P7AG4SBZ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regularizing quantum loss landscapes by noise injection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Aleksey K. Fedorov, Alena S. Mastiukova, Daniil S. Bagaev, Maxim A.Gavreev, Nikita A. Nemkov","submitted_at":"2025-05-13T17:28:08Z","abstract_excerpt":"The difficulty of training variational quantum algorithms and quantum machine learning models is well established. In particular, quantum loss landscapes are often highly non-convex and dominated by poor local minima. While this renders their training NP-hard in general, efficient heuristics that work well for typical instances may still exist. Here, we propose a protocol that uses a targeted noise injection to smooth and regularize quantum loss landscapes. It works by exponentially suppressing the high-frequency components in the Fourier expansion of the quantum loss function. The protocol ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08759","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.08759/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":"2026-06-09T01:05:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f4m9xZmWIKZ2HjoglOsBuy14305ikafY+m3UQbPv+5nLbn4SiVQHgw4t1SllNVlmE6wDAKFt5k1shvy72BMpBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T00:52:59.095214Z"},"content_sha256":"43a00ded29cbea08c30aec94b76a49b1c19de87035fa88df71ac00845874468d","schema_version":"1.0","event_id":"sha256:43a00ded29cbea08c30aec94b76a49b1c19de87035fa88df71ac00845874468d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/bundle.json","state_url":"https://pith.science/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/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":"2026-06-11T00:52:59Z","links":{"resolver":"https://pith.science/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6","bundle":"https://pith.science/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/bundle.json","state":"https://pith.science/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3GUZQLTLHAGJ5HQ2P7AG4SBZ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:J3GUZQLTLHAGJ5HQ2P7AG4SBZ6","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":"2677efd68a9f2aafcb13d0078ec4bd00cb48f6261097503a8cf1971c8c75d2bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-13T17:28:08Z","title_canon_sha256":"3c916d78261d1ce534f7e9d02c015313b1a3a21c15560245d98495eb2a8f9bc5"},"schema_version":"1.0","source":{"id":"2505.08759","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08759","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08759v1","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08759","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_12","alias_value":"J3GUZQLTLHAG","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_16","alias_value":"J3GUZQLTLHAGJ5HQ","created_at":"2026-06-09T01:05:06Z"},{"alias_kind":"pith_short_8","alias_value":"J3GUZQLT","created_at":"2026-06-09T01:05:06Z"}],"graph_snapshots":[{"event_id":"sha256:43a00ded29cbea08c30aec94b76a49b1c19de87035fa88df71ac00845874468d","target":"graph","created_at":"2026-06-09T01:05:06Z","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/2505.08759/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The difficulty of training variational quantum algorithms and quantum machine learning models is well established. In particular, quantum loss landscapes are often highly non-convex and dominated by poor local minima. While this renders their training NP-hard in general, efficient heuristics that work well for typical instances may still exist. Here, we propose a protocol that uses a targeted noise injection to smooth and regularize quantum loss landscapes. It works by exponentially suppressing the high-frequency components in the Fourier expansion of the quantum loss function. The protocol ca","authors_text":"Aleksey K. Fedorov, Alena S. Mastiukova, Daniil S. Bagaev, Maxim A.Gavreev, Nikita A. Nemkov","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-13T17:28:08Z","title":"Regularizing quantum loss landscapes by noise injection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08759","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":"sha256:62a8f192db917813c1414594c77edb0824d28b17bf99e807fb45cdb8cd58cee5","target":"record","created_at":"2026-06-09T01:05:06Z","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":"2677efd68a9f2aafcb13d0078ec4bd00cb48f6261097503a8cf1971c8c75d2bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-05-13T17:28:08Z","title_canon_sha256":"3c916d78261d1ce534f7e9d02c015313b1a3a21c15560245d98495eb2a8f9bc5"},"schema_version":"1.0","source":{"id":"2505.08759","kind":"arxiv","version":1}},"canonical_sha256":"4ecd4cc17359c064f4f0d3fe037241cfbc0e0de60fa0d3cc15c06bc13ecf8180","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ecd4cc17359c064f4f0d3fe037241cfbc0e0de60fa0d3cc15c06bc13ecf8180","first_computed_at":"2026-06-09T01:05:06.564270Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:06.564270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pbTuE1PMboEp4fvttY2AXdU0syCVCQ5w02ojn6uQENT4HZIz6G/w4aWAqNcbYnCS/e9No9WXtlo0hQd48c4pAg==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:06.564798Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.08759","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62a8f192db917813c1414594c77edb0824d28b17bf99e807fb45cdb8cd58cee5","sha256:43a00ded29cbea08c30aec94b76a49b1c19de87035fa88df71ac00845874468d"],"state_sha256":"4f41d0e63ac73ad7e5a853fb2810b01a6935ba8e62560d247d5a56bea1d37b54"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"poZhD1Zkha+588NLEz1oG4jjKcZHgUZVE1dJ3ZBKvVAqQPCbRdjp8d8TFWEVjq1RhBhdyfu24bQmGRaOzR/yBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T00:52:59.099194Z","bundle_sha256":"9439b076226a78c7200b62584c160a6c975b76726cffcf12332f8feaf78f25fe"}}