{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NILXE5M5KOQEPEXRRO6LS5BNF5","short_pith_number":"pith:NILXE5M5","schema_version":"1.0","canonical_sha256":"6a1772759d53a04792f18bbcb9742d2f5aa8ceea191948537d990cc3094b93ee","source":{"kind":"arxiv","id":"2501.16604","version":1},"attestation_state":"computed","paper":{"title":"Ultrafast neuromorphic computing with nanophotonic optical parametric oscillators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.optics","authors_text":"Alireza Marandi, Arkadev Roy, Gordon H.Y. Li, James Williams, Luis L. Ledezma, Midya Parto, Robert M. Gray, Ryoto Sekine","submitted_at":"2025-01-28T00:44:06Z","abstract_excerpt":"Over the past decade, artificial intelligence (AI) has led to disruptive advancements in fundamental sciences and everyday technologies. Among various machine learning algorithms, deep neural networks have become instrumental in revealing complex patterns in large datasets with key applications in computer vision, natural language processing, and predictive analytics. On-chip photonic neural networks offer a promising platform that leverage high bandwidths and low propagation losses associated with optical signals to perform analog computations for deep learning. However, nanophotonic circuits"},"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":"2501.16604","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2025-01-28T00:44:06Z","cross_cats_sorted":[],"title_canon_sha256":"64102c979b325eb232bf2cf3ae2637dbd4733c23451fdd7f57a555306107ba7b","abstract_canon_sha256":"89b67ff8ca1949deb3fc8766a325e0aa5fda23ae29b9a26a33892368376bd359"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:15.704219Z","signature_b64":"3I3hSSLhe71iLqmyPpJCy45VhAotPFskU42M9PoNWGdxmqces0kjVNitga5DflkFu9dAyXLnDCra72qZ8jusDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a1772759d53a04792f18bbcb9742d2f5aa8ceea191948537d990cc3094b93ee","last_reissued_at":"2026-07-05T10:06:15.703725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:15.703725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ultrafast neuromorphic computing with nanophotonic optical parametric oscillators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.optics","authors_text":"Alireza Marandi, Arkadev Roy, Gordon H.Y. Li, James Williams, Luis L. Ledezma, Midya Parto, Robert M. Gray, Ryoto Sekine","submitted_at":"2025-01-28T00:44:06Z","abstract_excerpt":"Over the past decade, artificial intelligence (AI) has led to disruptive advancements in fundamental sciences and everyday technologies. Among various machine learning algorithms, deep neural networks have become instrumental in revealing complex patterns in large datasets with key applications in computer vision, natural language processing, and predictive analytics. On-chip photonic neural networks offer a promising platform that leverage high bandwidths and low propagation losses associated with optical signals to perform analog computations for deep learning. However, nanophotonic circuits"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16604","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/2501.16604/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":"2501.16604","created_at":"2026-07-05T10:06:15.703790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16604v1","created_at":"2026-07-05T10:06:15.703790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16604","created_at":"2026-07-05T10:06:15.703790+00:00"},{"alias_kind":"pith_short_12","alias_value":"NILXE5M5KOQE","created_at":"2026-07-05T10:06:15.703790+00:00"},{"alias_kind":"pith_short_16","alias_value":"NILXE5M5KOQEPEXR","created_at":"2026-07-05T10:06:15.703790+00:00"},{"alias_kind":"pith_short_8","alias_value":"NILXE5M5","created_at":"2026-07-05T10:06:15.703790+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.21861","citing_title":"Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5","json":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5.json","graph_json":"https://pith.science/api/pith-number/NILXE5M5KOQEPEXRRO6LS5BNF5/graph.json","events_json":"https://pith.science/api/pith-number/NILXE5M5KOQEPEXRRO6LS5BNF5/events.json","paper":"https://pith.science/paper/NILXE5M5"},"agent_actions":{"view_html":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5","download_json":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5.json","view_paper":"https://pith.science/paper/NILXE5M5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16604&json=true","fetch_graph":"https://pith.science/api/pith-number/NILXE5M5KOQEPEXRRO6LS5BNF5/graph.json","fetch_events":"https://pith.science/api/pith-number/NILXE5M5KOQEPEXRRO6LS5BNF5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5/action/storage_attestation","attest_author":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5/action/author_attestation","sign_citation":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5/action/citation_signature","submit_replication":"https://pith.science/pith/NILXE5M5KOQEPEXRRO6LS5BNF5/action/replication_record"}},"created_at":"2026-07-05T10:06:15.703790+00:00","updated_at":"2026-07-05T10:06:15.703790+00:00"}