{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TYNZBTQX6Q7Z7VNLF5K4GU72G2","short_pith_number":"pith:TYNZBTQX","canonical_record":{"source":{"id":"2501.12776","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-01-22T10:21:00Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"title_canon_sha256":"132eda7ce98fe246c8ed06f8945088b61364411cc3fae6bc2f4a032717c05385","abstract_canon_sha256":"e0211142dbbd5b6e2720352002d1ed00e1dabe53e33c138e64f274f7c67d0a08"},"schema_version":"1.0"},"canonical_sha256":"9e1b90ce17f43f9fd5ab2f55c353fa369a42ea8114c482a31f574ee63099665d","source":{"kind":"arxiv","id":"2501.12776","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.12776","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"arxiv_version","alias_value":"2501.12776v1","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12776","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_12","alias_value":"TYNZBTQX6Q7Z","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_16","alias_value":"TYNZBTQX6Q7Z7VNL","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_8","alias_value":"TYNZBTQX","created_at":"2026-07-05T10:03:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TYNZBTQX6Q7Z7VNLF5K4GU72G2","target":"record","payload":{"canonical_record":{"source":{"id":"2501.12776","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-01-22T10:21:00Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"title_canon_sha256":"132eda7ce98fe246c8ed06f8945088b61364411cc3fae6bc2f4a032717c05385","abstract_canon_sha256":"e0211142dbbd5b6e2720352002d1ed00e1dabe53e33c138e64f274f7c67d0a08"},"schema_version":"1.0"},"canonical_sha256":"9e1b90ce17f43f9fd5ab2f55c353fa369a42ea8114c482a31f574ee63099665d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:58.524683Z","signature_b64":"pZ69jzz0lYWDnpWR0784TABnehP231YghACCg6xbyA+l8yBbQNzHvF+v7WMX7c53Dba0qfkFrQxe3caQbHBLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e1b90ce17f43f9fd5ab2f55c353fa369a42ea8114c482a31f574ee63099665d","last_reissued_at":"2026-07-05T10:03:58.524168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:58.524168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.12776","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":"2026-07-05T10:03:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RbNt4AYa8Ud4JNj5v+4jEDtrKVpkAFuZYGbkQVTEkrV8H1qGLGZ5btcWYXarSEXa+q84XhwzhZ6HjEnpkrXeBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T03:51:13.779474Z"},"content_sha256":"458a97d0ae9eba0daf44575a5a3ab3c744b627bd989d7874847c50aa825aa7b7","schema_version":"1.0","event_id":"sha256:458a97d0ae9eba0daf44575a5a3ab3c744b627bd989d7874847c50aa825aa7b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TYNZBTQX6Q7Z7VNLF5K4GU72G2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"quant-ph","authors_text":"Alexis Askitopoulos, Davit Aghamalyan, Eleni I. Vlahogianni, Konstantinos Blazakis, Negin Alisoltani, Nikolaos Schetakis, Panagiotis Fafoutellis, Paolo Bonfini, Symeon I. Tsintzos","submitted_at":"2025-01-22T10:21:00Z","abstract_excerpt":"Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12776","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/2501.12776/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":"2026-07-05T10:03:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PxriHNsLf8kIKA5ypGf2kvQg7nfD0AkjFb36ZHRHqUNvr9zi+xk+KY8WaxbbY/uggj+wDfRxHE7Y9M2intXSBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T03:51:13.779852Z"},"content_sha256":"741565b5e8464697efea4e2731046a940b7ac58a6c4c07b5ff74e1ba2a30d900","schema_version":"1.0","event_id":"sha256:741565b5e8464697efea4e2731046a940b7ac58a6c4c07b5ff74e1ba2a30d900"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/bundle.json","state_url":"https://pith.science/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/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":"2026-07-06T03:51:13Z","links":{"resolver":"https://pith.science/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2","bundle":"https://pith.science/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/bundle.json","state":"https://pith.science/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TYNZBTQX6Q7Z7VNLF5K4GU72G2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TYNZBTQX6Q7Z7VNLF5K4GU72G2","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":"e0211142dbbd5b6e2720352002d1ed00e1dabe53e33c138e64f274f7c67d0a08","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-01-22T10:21:00Z","title_canon_sha256":"132eda7ce98fe246c8ed06f8945088b61364411cc3fae6bc2f4a032717c05385"},"schema_version":"1.0","source":{"id":"2501.12776","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.12776","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"arxiv_version","alias_value":"2501.12776v1","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12776","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_12","alias_value":"TYNZBTQX6Q7Z","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_16","alias_value":"TYNZBTQX6Q7Z7VNL","created_at":"2026-07-05T10:03:58Z"},{"alias_kind":"pith_short_8","alias_value":"TYNZBTQX","created_at":"2026-07-05T10:03:58Z"}],"graph_snapshots":[{"event_id":"sha256:741565b5e8464697efea4e2731046a940b7ac58a6c4c07b5ff74e1ba2a30d900","target":"graph","created_at":"2026-07-05T10:03:58Z","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/2501.12776/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we in","authors_text":"Alexis Askitopoulos, Davit Aghamalyan, Eleni I. Vlahogianni, Konstantinos Blazakis, Negin Alisoltani, Nikolaos Schetakis, Panagiotis Fafoutellis, Paolo Bonfini, Symeon I. Tsintzos","cross_cats":["cs.AI","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-01-22T10:21:00Z","title":"Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12776","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":"sha256:458a97d0ae9eba0daf44575a5a3ab3c744b627bd989d7874847c50aa825aa7b7","target":"record","created_at":"2026-07-05T10:03:58Z","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":"e0211142dbbd5b6e2720352002d1ed00e1dabe53e33c138e64f274f7c67d0a08","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-01-22T10:21:00Z","title_canon_sha256":"132eda7ce98fe246c8ed06f8945088b61364411cc3fae6bc2f4a032717c05385"},"schema_version":"1.0","source":{"id":"2501.12776","kind":"arxiv","version":1}},"canonical_sha256":"9e1b90ce17f43f9fd5ab2f55c353fa369a42ea8114c482a31f574ee63099665d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e1b90ce17f43f9fd5ab2f55c353fa369a42ea8114c482a31f574ee63099665d","first_computed_at":"2026-07-05T10:03:58.524168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:03:58.524168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pZ69jzz0lYWDnpWR0784TABnehP231YghACCg6xbyA+l8yBbQNzHvF+v7WMX7c53Dba0qfkFrQxe3caQbHBLCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:03:58.524683Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.12776","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:458a97d0ae9eba0daf44575a5a3ab3c744b627bd989d7874847c50aa825aa7b7","sha256:741565b5e8464697efea4e2731046a940b7ac58a6c4c07b5ff74e1ba2a30d900"],"state_sha256":"172bb61b30652becc086b6cbbb79f26b455de785206d20d58be34ca8436c7004"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"09gmrCcskYFA774iuztyRjqbFl0VQeYbZJriXo2Flo4Ky5nTzfgicmL77UBjgbikVczsX4opNRjgwr4T9MmhCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T03:51:13.782234Z","bundle_sha256":"f43a5a93ab2b9787be514bdfa4864f6e233a8ff15f1a15a0b352ea3718a34fb1"}}