{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:W4AXC3OFH7GE7URVPNFHS2YZME","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":"baff2e145198bd4fc8eec9cf920986741d165462802d379a0551f1edd122048d","cross_cats_sorted":["math-ph","math.MP","math.PR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-05-17T20:09:26Z","title_canon_sha256":"81a2748fb883af1357e76158657478d3deef61494e800ff174e44a84583e8f77"},"schema_version":"1.0","source":{"id":"2605.18912","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18912","created_at":"2026-05-20T00:06:31Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18912v1","created_at":"2026-05-20T00:06:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18912","created_at":"2026-05-20T00:06:31Z"},{"alias_kind":"pith_short_12","alias_value":"W4AXC3OFH7GE","created_at":"2026-05-20T00:06:31Z"},{"alias_kind":"pith_short_16","alias_value":"W4AXC3OFH7GE7URV","created_at":"2026-05-20T00:06:31Z"},{"alias_kind":"pith_short_8","alias_value":"W4AXC3OF","created_at":"2026-05-20T00:06:31Z"}],"graph_snapshots":[{"event_id":"sha256:7b1660e57a052049133b245de795b481b307497e339d9040794c53342c00b127","target":"graph","created_at":"2026-05-20T00:06:31Z","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/2605.18912/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a quantum Viterbi decoding algorithm for hidden quantum Markov models (HQMMs) motivated by quantum information processing and quantum algorithms. Given a finite sequence of measurement outcomes, the algorithm identifies hidden quantum trajectories that maximize a joint decoding functional, serving as a genuine quantum analogue of the classical Viterbi score. Unlike classical hidden Markov models, where decoding optimizes over a finite discrete state space, our method performs optimization over a continuous manifold of pure quantum effects, thereby exploiting coherent superposition","authors_text":"Abdessatar Souissi, El Gheteb Soueidi, Farrukh Mukhamedov, Luigi Accardi, Mohamed Rhaima","cross_cats":["math-ph","math.MP","math.PR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-05-17T20:09:26Z","title":"Quantum Viterbi Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18912","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":"sha256:08462cd79259f5fc641f3a9385b7d44df0232b735b4a721d6ae1dff90c36d77e","target":"record","created_at":"2026-05-20T00:06:31Z","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":"baff2e145198bd4fc8eec9cf920986741d165462802d379a0551f1edd122048d","cross_cats_sorted":["math-ph","math.MP","math.PR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-05-17T20:09:26Z","title_canon_sha256":"81a2748fb883af1357e76158657478d3deef61494e800ff174e44a84583e8f77"},"schema_version":"1.0","source":{"id":"2605.18912","kind":"arxiv","version":1}},"canonical_sha256":"b701716dc53fcc4fd2357b4a796b1961171486ef11e59dc06b949c742f5d9599","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b701716dc53fcc4fd2357b4a796b1961171486ef11e59dc06b949c742f5d9599","first_computed_at":"2026-05-20T00:06:31.794933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:31.794933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EQE13VbWrJ94/I88U1WVgvuX9t47Cyzopj2zCCI9BfFP5qMMf0UII+ks5mzXx/3qxAXEu2cmwnNjyWWRor2BBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:31.795666Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18912","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08462cd79259f5fc641f3a9385b7d44df0232b735b4a721d6ae1dff90c36d77e","sha256:7b1660e57a052049133b245de795b481b307497e339d9040794c53342c00b127"],"state_sha256":"fc2ab173eef6e59a00a539beb8e0293644ff62bea9ad09e9aff13d5431862729"}