{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GR5DKCFUKKBHJKM57OKWPC2G6I","short_pith_number":"pith:GR5DKCFU","schema_version":"1.0","canonical_sha256":"347a3508b4528274a99dfb95678b46f20a15347ec62fc67d991c1b960169487f","source":{"kind":"arxiv","id":"2309.07056","version":2},"attestation_state":"computed","paper":{"title":"Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Carlos Ruiz-Gonzalez, Ebrahim Karimi, Mario Krenn, S\\\"oren Arlt, Tareq Jaouni, Xuemei Gu","submitted_at":"2023-09-13T16:13:54Z","abstract_excerpt":"Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called $inception$ or $deep$ $dreaming$, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we \"invert\" the neural network -- effectively asking how it imagines a quantum syste"},"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":"2309.07056","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2023-09-13T16:13:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"724d9053630d7a79d7882e3650358de0410e29388cc81f7e702266d6b585fd3d","abstract_canon_sha256":"023de33127421f9520e16a7c1eea449c0a67d4854ba47010b78db71a0feb8009"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:45:48.738992Z","signature_b64":"YcEa/2AYh5NnKfe4ZYWqg7YX9hWSnNdqpun+8joswgLZCHqZ0/RkiZlbW+tor6OXtOyRbEAxAFIGcaK0NzJ1AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"347a3508b4528274a99dfb95678b46f20a15347ec62fc67d991c1b960169487f","last_reissued_at":"2026-07-05T07:45:48.738378Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:45:48.738378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Carlos Ruiz-Gonzalez, Ebrahim Karimi, Mario Krenn, S\\\"oren Arlt, Tareq Jaouni, Xuemei Gu","submitted_at":"2023-09-13T16:13:54Z","abstract_excerpt":"Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called $inception$ or $deep$ $dreaming$, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we \"invert\" the neural network -- effectively asking how it imagines a quantum syste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07056","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/2309.07056/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":"2309.07056","created_at":"2026-07-05T07:45:48.738455+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.07056v2","created_at":"2026-07-05T07:45:48.738455+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07056","created_at":"2026-07-05T07:45:48.738455+00:00"},{"alias_kind":"pith_short_12","alias_value":"GR5DKCFUKKBH","created_at":"2026-07-05T07:45:48.738455+00:00"},{"alias_kind":"pith_short_16","alias_value":"GR5DKCFUKKBHJKM5","created_at":"2026-07-05T07:45:48.738455+00:00"},{"alias_kind":"pith_short_8","alias_value":"GR5DKCFU","created_at":"2026-07-05T07:45:48.738455+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/GR5DKCFUKKBHJKM57OKWPC2G6I","json":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I.json","graph_json":"https://pith.science/api/pith-number/GR5DKCFUKKBHJKM57OKWPC2G6I/graph.json","events_json":"https://pith.science/api/pith-number/GR5DKCFUKKBHJKM57OKWPC2G6I/events.json","paper":"https://pith.science/paper/GR5DKCFU"},"agent_actions":{"view_html":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I","download_json":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I.json","view_paper":"https://pith.science/paper/GR5DKCFU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.07056&json=true","fetch_graph":"https://pith.science/api/pith-number/GR5DKCFUKKBHJKM57OKWPC2G6I/graph.json","fetch_events":"https://pith.science/api/pith-number/GR5DKCFUKKBHJKM57OKWPC2G6I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I/action/storage_attestation","attest_author":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I/action/author_attestation","sign_citation":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I/action/citation_signature","submit_replication":"https://pith.science/pith/GR5DKCFUKKBHJKM57OKWPC2G6I/action/replication_record"}},"created_at":"2026-07-05T07:45:48.738455+00:00","updated_at":"2026-07-05T07:45:48.738455+00:00"}