{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:Q2ECSRD566WCFU6Q7VHLTGGBSM","short_pith_number":"pith:Q2ECSRD5","schema_version":"1.0","canonical_sha256":"868829447df7ac22d3d0fd4eb998c19325c7c04088c3fcc29bfa8b8d9ad3ca99","source":{"kind":"arxiv","id":"2305.05881","version":2},"attestation_state":"computed","paper":{"title":"Parallel hybrid quantum-classical machine learning for kernelized time-series classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Ara Ghukasyan, Gilchan Park, Jack S. Baker, Kwangmin Yu, Oktay Goktas, Santosh Kumar Radha","submitted_at":"2023-05-10T04:01:15Z","abstract_excerpt":"Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a time-series Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multi"},"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":"2305.05881","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2023-05-10T04:01:15Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"c8a3b55858ace641393ff6ab00c0eaf9883cc9708ce92d75e31fdcf6eac81091","abstract_canon_sha256":"b867df9279c07021b47f957632ccf085f88003c2a13b5d5091adc06c9c73f472"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:17.897664Z","signature_b64":"/4sWVjfdQnA02XFj7YqEYZfG1c0q50VWz6iPAX6LRJVqGpdtYBuORGSZC3OwUiJWJvIpqGug6x934sMbZIztDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"868829447df7ac22d3d0fd4eb998c19325c7c04088c3fcc29bfa8b8d9ad3ca99","last_reissued_at":"2026-07-05T07:46:17.897157Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:17.897157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel hybrid quantum-classical machine learning for kernelized time-series classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"quant-ph","authors_text":"Ara Ghukasyan, Gilchan Park, Jack S. Baker, Kwangmin Yu, Oktay Goktas, Santosh Kumar Radha","submitted_at":"2023-05-10T04:01:15Z","abstract_excerpt":"Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a time-series Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05881","kind":"arxiv","version":2},"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/2305.05881/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":"2305.05881","created_at":"2026-07-05T07:46:17.897215+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.05881v2","created_at":"2026-07-05T07:46:17.897215+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05881","created_at":"2026-07-05T07:46:17.897215+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2ECSRD566WC","created_at":"2026-07-05T07:46:17.897215+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2ECSRD566WCFU6Q","created_at":"2026-07-05T07:46:17.897215+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2ECSRD5","created_at":"2026-07-05T07:46:17.897215+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.05479","citing_title":"Quantum Simulation of the Real-time Dynamics in the multi-flavor Gross-Neveu Model at the utility scale using Superconducting Quantum Computers","ref_index":61,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM","json":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM.json","graph_json":"https://pith.science/api/pith-number/Q2ECSRD566WCFU6Q7VHLTGGBSM/graph.json","events_json":"https://pith.science/api/pith-number/Q2ECSRD566WCFU6Q7VHLTGGBSM/events.json","paper":"https://pith.science/paper/Q2ECSRD5"},"agent_actions":{"view_html":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM","download_json":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM.json","view_paper":"https://pith.science/paper/Q2ECSRD5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.05881&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2ECSRD566WCFU6Q7VHLTGGBSM/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2ECSRD566WCFU6Q7VHLTGGBSM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM/action/storage_attestation","attest_author":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM/action/author_attestation","sign_citation":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM/action/citation_signature","submit_replication":"https://pith.science/pith/Q2ECSRD566WCFU6Q7VHLTGGBSM/action/replication_record"}},"created_at":"2026-07-05T07:46:17.897215+00:00","updated_at":"2026-07-05T07:46:17.897215+00:00"}