{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JDXENLMUKJH3NCHMO4AQ5QBQEX","short_pith_number":"pith:JDXENLMU","schema_version":"1.0","canonical_sha256":"48ee46ad94524fb688ec77010ec03025fda459539fadd19c6b4ab7dc3e601996","source":{"kind":"arxiv","id":"2210.07776","version":2},"attestation_state":"computed","paper":{"title":"Optimizing optical potentials with physics-inspired learning algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.atom-ph","physics.optics"],"primary_cat":"cond-mat.quant-gas","authors_text":"Andreas Deutschmann-Olek, Andreas Kugi, Felix Motzoi, J\\\"org Schmiedmayer, Martino Calzavara, Maximilian Pr\\\"ufer, Sebastian Erne, Tommaso Calarco, Yevhenii Kuriatnikov","submitted_at":"2022-10-14T13:03:07Z","abstract_excerpt":"We present our new experimental and theoretical framework which combines a broadband superluminescent diode (SLED/SLD) with fast learning algorithms to provide speed and accuracy improvements for the optimization of 1D optical dipole potentials, here generated with a Digital Micromirror Device (DMD). To characterize the setup and potential speckle patterns arising from coherence, we compare the superluminescent diode to a single-mode laser by investigating interference properties. We employ Machine Learning (ML) tools to train a physics-inspired model acting as a digital twin of the optical sy"},"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":"2210.07776","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.quant-gas","submitted_at":"2022-10-14T13:03:07Z","cross_cats_sorted":["physics.atom-ph","physics.optics"],"title_canon_sha256":"80c706134a277461cf26baec721356ff140ffd333fe04434a291b665ec9dda45","abstract_canon_sha256":"eb680843e01be48c20d77ee5f2af6267eda35bb7a9d2b8a71311e0a117a3cc2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:05.951782Z","signature_b64":"muh17/0XIt8BHRdIq5ZnmT4kQ1JmSCrmVGoqo1uSGUQ/UYJZ0nY6cqctHKbC+eWWeyqz5vV6bTr6p1dJlGTKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48ee46ad94524fb688ec77010ec03025fda459539fadd19c6b4ab7dc3e601996","last_reissued_at":"2026-07-05T06:09:05.951305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:05.951305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing optical potentials with physics-inspired learning algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.atom-ph","physics.optics"],"primary_cat":"cond-mat.quant-gas","authors_text":"Andreas Deutschmann-Olek, Andreas Kugi, Felix Motzoi, J\\\"org Schmiedmayer, Martino Calzavara, Maximilian Pr\\\"ufer, Sebastian Erne, Tommaso Calarco, Yevhenii Kuriatnikov","submitted_at":"2022-10-14T13:03:07Z","abstract_excerpt":"We present our new experimental and theoretical framework which combines a broadband superluminescent diode (SLED/SLD) with fast learning algorithms to provide speed and accuracy improvements for the optimization of 1D optical dipole potentials, here generated with a Digital Micromirror Device (DMD). To characterize the setup and potential speckle patterns arising from coherence, we compare the superluminescent diode to a single-mode laser by investigating interference properties. We employ Machine Learning (ML) tools to train a physics-inspired model acting as a digital twin of the optical sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07776","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/2210.07776/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":"2210.07776","created_at":"2026-07-05T06:09:05.951371+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.07776v2","created_at":"2026-07-05T06:09:05.951371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07776","created_at":"2026-07-05T06:09:05.951371+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDXENLMUKJH3","created_at":"2026-07-05T06:09:05.951371+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDXENLMUKJH3NCHM","created_at":"2026-07-05T06:09:05.951371+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDXENLMU","created_at":"2026-07-05T06:09:05.951371+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03974","citing_title":"Real-time adaptive quantum error correction by model-free multi-agent learning","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX","json":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX.json","graph_json":"https://pith.science/api/pith-number/JDXENLMUKJH3NCHMO4AQ5QBQEX/graph.json","events_json":"https://pith.science/api/pith-number/JDXENLMUKJH3NCHMO4AQ5QBQEX/events.json","paper":"https://pith.science/paper/JDXENLMU"},"agent_actions":{"view_html":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX","download_json":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX.json","view_paper":"https://pith.science/paper/JDXENLMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.07776&json=true","fetch_graph":"https://pith.science/api/pith-number/JDXENLMUKJH3NCHMO4AQ5QBQEX/graph.json","fetch_events":"https://pith.science/api/pith-number/JDXENLMUKJH3NCHMO4AQ5QBQEX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX/action/storage_attestation","attest_author":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX/action/author_attestation","sign_citation":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX/action/citation_signature","submit_replication":"https://pith.science/pith/JDXENLMUKJH3NCHMO4AQ5QBQEX/action/replication_record"}},"created_at":"2026-07-05T06:09:05.951371+00:00","updated_at":"2026-07-05T06:09:05.951371+00:00"}