{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OHT2IV57V3PKA7C6QVYNNLOX6J","short_pith_number":"pith:OHT2IV57","schema_version":"1.0","canonical_sha256":"71e7a457bfaedea07c5e8570d6add7f250b9a372b09e25517bb7cdf7177d85e6","source":{"kind":"arxiv","id":"2606.27815","version":1},"attestation_state":"computed","paper":{"title":"Quantum Dynamic Time Warping for Multivariate Time Series Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Alejandro Mayorga-Redondo, Diego Alvarez-Estevez, Eduardo Mosqueira-Rey","submitted_at":"2026-06-26T07:58:37Z","abstract_excerpt":"Dynamic Time Warping (DTW) is a cornerstone for time series classification, but its reliance on Euclidean distances fails to capture latent cross-channel correlations in complex multivariate data. We propose a hybrid Quantum Dynamic Time Warping (qDTW) architecture, replacing the classical distance metric with the parameterized geometry of a quantum Hilbert space. Through structural ablation on benchmarks up to $C=8$ spatial dimensions, we establish fundamental topological rules for quantum sequence alignment.\n  We introduce a Unified Pre-Embedding Adjoint Ansatz that decouples trainable entan"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.27815","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-06-26T07:58:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"22ef41083b803f1a0546b9c2d00ef013caf2d0c15aea088b3cbf1a42fb95cf1e","abstract_canon_sha256":"364f9fff8d479d6e61554ad155c216a67ba106fbfc669fee0f96fa11de5fa709"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:49.276360Z","signature_b64":"rzXjtn/Riy4D9tAQ7llkBXKyC14nnFYTYyDrIzw8qdDPBYWp5xKNy2MtUIXZOjtMCxJgtz2zCgx6USO9HTIVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71e7a457bfaedea07c5e8570d6add7f250b9a372b09e25517bb7cdf7177d85e6","last_reissued_at":"2026-06-29T01:14:49.275927Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:49.275927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Dynamic Time Warping for Multivariate Time Series Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Alejandro Mayorga-Redondo, Diego Alvarez-Estevez, Eduardo Mosqueira-Rey","submitted_at":"2026-06-26T07:58:37Z","abstract_excerpt":"Dynamic Time Warping (DTW) is a cornerstone for time series classification, but its reliance on Euclidean distances fails to capture latent cross-channel correlations in complex multivariate data. We propose a hybrid Quantum Dynamic Time Warping (qDTW) architecture, replacing the classical distance metric with the parameterized geometry of a quantum Hilbert space. Through structural ablation on benchmarks up to $C=8$ spatial dimensions, we establish fundamental topological rules for quantum sequence alignment.\n  We introduce a Unified Pre-Embedding Adjoint Ansatz that decouples trainable entan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27815","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/2606.27815/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.27815","created_at":"2026-06-29T01:14:49.275998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27815v1","created_at":"2026-06-29T01:14:49.275998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27815","created_at":"2026-06-29T01:14:49.275998+00:00"},{"alias_kind":"pith_short_12","alias_value":"OHT2IV57V3PK","created_at":"2026-06-29T01:14:49.275998+00:00"},{"alias_kind":"pith_short_16","alias_value":"OHT2IV57V3PKA7C6","created_at":"2026-06-29T01:14:49.275998+00:00"},{"alias_kind":"pith_short_8","alias_value":"OHT2IV57","created_at":"2026-06-29T01:14:49.275998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J","json":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J.json","graph_json":"https://pith.science/api/pith-number/OHT2IV57V3PKA7C6QVYNNLOX6J/graph.json","events_json":"https://pith.science/api/pith-number/OHT2IV57V3PKA7C6QVYNNLOX6J/events.json","paper":"https://pith.science/paper/OHT2IV57"},"agent_actions":{"view_html":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J","download_json":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J.json","view_paper":"https://pith.science/paper/OHT2IV57","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27815&json=true","fetch_graph":"https://pith.science/api/pith-number/OHT2IV57V3PKA7C6QVYNNLOX6J/graph.json","fetch_events":"https://pith.science/api/pith-number/OHT2IV57V3PKA7C6QVYNNLOX6J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J/action/storage_attestation","attest_author":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J/action/author_attestation","sign_citation":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J/action/citation_signature","submit_replication":"https://pith.science/pith/OHT2IV57V3PKA7C6QVYNNLOX6J/action/replication_record"}},"created_at":"2026-06-29T01:14:49.275998+00:00","updated_at":"2026-06-29T01:14:49.275998+00:00"}