{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AGLWPCMIKCE4MPUMEC32XP6Y66","short_pith_number":"pith:AGLWPCMI","schema_version":"1.0","canonical_sha256":"01976789885089c63e8c20b7abbfd8f788b83dc7c2eb24f80c86ef711477ad04","source":{"kind":"arxiv","id":"1901.05230","version":1},"attestation_state":"computed","paper":{"title":"Machine learning applied to quantum synchronization-assisted probing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"quant-ph","authors_text":"Gabriel Garau Estarellas, Gian Luca Giorgi, Miguel C. Soriano, Roberta Zambrini","submitted_at":"2019-01-16T11:03:23Z","abstract_excerpt":"A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both in-phase and in anti-phase. We analyze the capability of machine learning in this probing scheme based on quantum synchronization. An artificial neural network is used to infer, from a probe observable, main dissipation features, such as the environment Ohmicity index. The effic"},"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":"1901.05230","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-01-16T11:03:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"31eecd7e3ce7f5661942b3e5cda46bba909abfa4455c539a6cdb34fbfa5f9199","abstract_canon_sha256":"06ef747c1ae33631c2c787d9c2dc529d8986f1886a8f951972133548dde8b432"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:11.424407Z","signature_b64":"mxFSsyB2ne8sHw4mbUMrjjhpvc+pMelXR02oJk8xZ7k2osFGUxXnHBTeu0BlBbOJSmowYAztbeGCdPYMAEjsAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01976789885089c63e8c20b7abbfd8f788b83dc7c2eb24f80c86ef711477ad04","last_reissued_at":"2026-05-17T23:56:11.423674Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:11.423674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning applied to quantum synchronization-assisted probing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"quant-ph","authors_text":"Gabriel Garau Estarellas, Gian Luca Giorgi, Miguel C. Soriano, Roberta Zambrini","submitted_at":"2019-01-16T11:03:23Z","abstract_excerpt":"A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both in-phase and in anti-phase. We analyze the capability of machine learning in this probing scheme based on quantum synchronization. An artificial neural network is used to infer, from a probe observable, main dissipation features, such as the environment Ohmicity index. The effic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05230","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":""},"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":"1901.05230","created_at":"2026-05-17T23:56:11.423767+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.05230v1","created_at":"2026-05-17T23:56:11.423767+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05230","created_at":"2026-05-17T23:56:11.423767+00:00"},{"alias_kind":"pith_short_12","alias_value":"AGLWPCMIKCE4","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AGLWPCMIKCE4MPUM","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AGLWPCMI","created_at":"2026-05-18T12:33:12.712433+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/AGLWPCMIKCE4MPUMEC32XP6Y66","json":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66.json","graph_json":"https://pith.science/api/pith-number/AGLWPCMIKCE4MPUMEC32XP6Y66/graph.json","events_json":"https://pith.science/api/pith-number/AGLWPCMIKCE4MPUMEC32XP6Y66/events.json","paper":"https://pith.science/paper/AGLWPCMI"},"agent_actions":{"view_html":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66","download_json":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66.json","view_paper":"https://pith.science/paper/AGLWPCMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.05230&json=true","fetch_graph":"https://pith.science/api/pith-number/AGLWPCMIKCE4MPUMEC32XP6Y66/graph.json","fetch_events":"https://pith.science/api/pith-number/AGLWPCMIKCE4MPUMEC32XP6Y66/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66/action/storage_attestation","attest_author":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66/action/author_attestation","sign_citation":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66/action/citation_signature","submit_replication":"https://pith.science/pith/AGLWPCMIKCE4MPUMEC32XP6Y66/action/replication_record"}},"created_at":"2026-05-17T23:56:11.423767+00:00","updated_at":"2026-05-17T23:56:11.423767+00:00"}